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What is Journal Impact Factor?

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Daunted by the idea of choosing the right journal for your paper? Don’t be. Metrics have become an everyday word in scholarship, in general. Within its many fields of research – if not all of them – they provide important data about a journal’s impact and relevance among its readers. In an era of information proliferation, it has become increasingly important to know where to capture the most attention and interest of your target audience.

So, whenever you are in doubt about which journal suits you better, don’t forget to browse its metrics; they will certainly help you with the decision-making process. Start, for example, with the Journal Impact Factor.

Impact factor (IF) is a measure of the number of times an average paper in a journal is cited, during a year. Clarivate Analytics releases the Journal Impact Factors annually as part of the Web of Science Journal Citation Reports®. Only journals listed in the Science Citation Index Expanded® (SCIE) and Social Sciences Citation Index® (SSCI) receive an Impact Factor.

What is a good impact factor for a scientific journal?

Impact Factors are used to measure the importance of a journal by calculating the number of times selected articles are cited within a particular year. Hence, the higher the number of citations or articles coming from a particular journal, or impact factor, the higher it is ranked. IF is also a powerful tool if you want to compare journals in the subject category.

Measuring a Journal Impact Factor:

• CiteScore metrics – helps to measure journal citation impact. Free, comprehensive, transparent and current metrics calculated using data from Scopus®, the largest abstract and citation database of peer-reviewed literature.
• SJR – or SCImago Journal Rank, is based on the concept of a transfer of prestige between journals via their citation links.
• SNIP – or Source Normalized Impact per Paper, is a sophisticated metric that accounts for field-specific differences in citation practices.
• JIF – or Journal Impact Factor is calculated by Clarivate Analytics as the average of the sum of the citations received in a given year to a journal’s previous two years of publications, divided by the sum of “citable” publications in the previous two years.
• H-index – Although originally conceived as an author-level metric, the H -index has been being applied to higher-order aggregations of research publications, including journals.

Deciding the perfect journal for your paper is an important step. Metrics are excellent tools to guide you through the process. However, we also recommend you not neglect a perfectly written text, not only scientific and grammatically but also fitting the chosen journal’s requirements and scope. At Elsevier, we provide text-editing services that aim to amend and adjust your manuscript, to increase its chances of a successful acceptance by your target journal. Although each journal has its own editorial team, the overall quality, language and whether the article is innovative may also play a role.

Language Editing Services by Elsevier Author Services:

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University Library

Introduction to Impact Factor and Other Research Metrics

• Types of Metrics

Impact Factor

• Identifying Journals
• More Resources

More Information

• About Journal Impact Factor Visit this article published by Thomson Reuters on journal impact factor to learn more about the bibliometric and how it measures importance.
• Annual Reviews Rankings in JCR Visit this page to see how Annual Review Journals currently rank in Journal Citation Reports.
• SCImago Journal and Country Rank The SCImago Journal & Country Rank is a publicly available portal that includes the journals and country scientific indicators developed from the information contained in the Scopus database.

Ask a Librarian

Impact factor,  or Journal Impact Factor,  is a measure of the frequency with which the "average article" published in a given scholarly journal has been cited in a particular year or period and is often used to measure or describe the importance of a particular journal to its field. Impact factor was originally developed by Eugene Garfield, the founder of Institute of Scientific Information, which is now a part of Clarivate Analytics. Journal Impact Factor can be found in the  Journal Citation Reports or the JCR, as it's commonly known. Over the years various organizations have been created similar journal-level metrics, such as  SCImago Journal & Country Rank .

This page describes how to find impact factor in Journal Citation Reports .

Journal Citation Reports

Clarivate Analytics (formerly Institute for Scientific Information (ISI)) ranks, evaluates, and compares journals within subject categories and publishes the results in Journal Citation Reports . Journal Citation Reports provides ranking for journals in science, technology, and the social sciences. For every journal, the database collects and/or calculates information such as:

• citation/article counts
• impact factor
• immediacy index
• cited half-life
• citing half-life
• source data listing
• citing journal listing
• cited journal listing
• publisher information
• subject categories

Find Journal Citation Reports (JCR)

Follow the instructions below to find the Journal Citation Reports using the Library's resources.

• Begin at the Library homepage .
• Click on the orange tab that says "Find Materials," then scroll down until you see a laptop icon with the words "Databases by Subject and A-Z"; click on the icon.
• Type journal citations reports in the search box on the left side of the screen and then click on the magnifying glass to search that title.
• Your result will say "Journal Citation Reports"; click on it. It might ask you to provide your Net ID and password if you are off campus.

Find the Impact Factor

• Once in the database you either search by journal title (if you know which journal you want to see) or browse by category, which will let you view journals by JIF by discipline.
• On the left side you can choose search criteria, like impact factor range, year, and if the journal is open access.
• It is important to choose the right edition based on your subject area, as you won't be able to see specific journals if you choose the wrong one. Once you have finished selecting what to search, click Submit.
• You can't access impact factors from last year because the calculations only happen every two years (i.e. if the current year is 2021 the farthest you can go back is 2020). Most people choose the most current year they can access.
• Journals limited by the subject area, publisher, or geographic region.
• View all journals in order to browse.
• Search for a specific journal if you already know its title

Once you find a journal, the JCR gives you information about the journal, including the journal's abbreviations, how often it is published each year, the publisher, and the ISSN. 

Controversy

Many people have questioned the legitimacy of impact factor. Here are a few reasons why:

• Impact factor focuses purely on the numbers. There is no consideration of qualitative elements that have become important in today's world.
• Impact factor fails to incorporate more recent ways of sharing and using research, including Twitter mentions and posts, citation management downloads, and news and community information.
• Because impact factor is based on citations in only indexed journals , it fails to incorporate statistics from journals that might not be indexed and other sources like conference papers (which are important in the social sciences).
• Basic or summary information is usually cited the most in academia. That means that journals that publish articles with basic information are more likely to have higher impact factors. Journals that publish obscure or innovative information might not have as high of an impact factor.
• Some argue that impact factor is encouraging scholars to stick with mainstream topics and research.
• Scholars don't always have to cite something for it to be influential. Sometimes researchers just read something and it influences them, regardless of if they cite it in a future paper or piece of research.
• The journals in the JCR are mostly published in English. This means that many international sources aren't included in the conversation.
• It has been argued that journals have the ability to skew impact factor for their own journal. Before publishing an author, they will ask the author to cite more articles within their journal so that their impact factor goes up. This is NOT a common occurrence but instead something we should be aware of.
• << Previous: Types of Metrics
• Next: Identifying Journals >>
• Last Updated: Feb 28, 2024 12:49 PM
• URL: https://guides.library.illinois.edu/impact

Scholarly Impact and Citation Analysis

• More Information
• Google Scholar
• Scopus: Citation Analysis for an Article or an Author
• WOS: Citation Analysis for an Author
• WOS: Finding Citing References for an Article
• Analyzing Journals Using JCR
• Analyzing Journals using Scopus
• Journal Impact Factor: What is it?
• Google Scholar Metrics
• Citation Count for Books

Questions? Contact the Librarian

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Journal Impact Factor--What is it?

Journal Impact Factor

An offshoot of citation analysis is Journal Impact Factor (JIF) which is used to sort or rank journals by their relative importance. The underlying assumption behind Impact Factors (IF) is that journals with high IF publish articles that are cited more often than journals with lower IF.

Impact factors may be used by:

• Authors to decide where to submit an article for publication.
• Libraries to make collection development decisions
• Academic departments to assess academic productivity
• Academic departments to make decisions on promotion and tenure.

Where to find Journal Impact Factors?

The most notable source for journal impact factors is the annual publication called the Journal Citation Reports (JCR) published by Thomson Scientific.

How is the Journal Impact Factor Calculated?

Thomson defines impact factor as, “The journal Impact Factor is the average number of times articles from the journal published in the past two years have been cited in the JCR year. The Impact Factor is calculated by dividing the number of citations in the JCR year by the total number of articles published in the two previous years. An Impact Factor of 1.0 means that, on average, the articles published one or two year ago have been cited one time. An Impact Factor of 2.5 means that, on average, the articles published one or two year ago have been cited two and a half times. Citing articles may be from the same journal; most citing articles are from different journals.”

A journal's impact factor for 2008 would be calculated by taking the number of citations in 2008 to articles that were published in 2007 and 2006 and dividing that number by the total number of articles published in that same journal in 2007 and 2006.Below is how Thomson calculated the 2008 impact factor for the journal Academy of Management Review :

Thus, the Impact Factor of 6.125 for the journal, Academy of Management Review for 2008 indicates that on average, the articles published in this journal in the past two years have been cited about 6.125 times.

Factors to Consider While Consulting Impact Factors:

Publication Date : The impact factor is based on citation frequency of articles from a journal in their first few years of publication. This does not serve well the journals with articles that get cited over a longer period of time (let's say, 10 years) rather than immediately. In other words, journals in rapidly expanding fields such as cell biology and computing tend to have much higher immediate citation rates leading to higher IFs than journals in fields like Education or Economics.

Journal Impact Factor not Article Impact Factor: Citations to articles in a journal are not evenly distributed. In fact, some articles in a journal may not be cited at all but a few highly cited articles could lead to a high IF. Therefore, the IF does not accurately reflect the quality of individual articles published in a journal. Also, journals with more issues and articles can have higher Impact Factors which could be misleading as it does not really reflect the quality of articles.

Review Articles: Review articles (which tend to receive more citations), editorials, letters, and news items are not counted in article total but if cited are counted as citations for the journal. This leaves room for manipulation of ratio used to calculate impact factors leading to inflated impact factors in some cases.

Clinical Journals: Clinical journals usually have low citation counts. This puts such journals at a disadvantage with research journals in the field that have higher citation counts.

Uneven Coverage : The Journal Citation Reports focuses much more on disciplines where the primary means of publishing is through journal article. It provides less coverage to areas in Social Sciences and Humanities, where books and other publishing formats are more prevalent.

• << Previous: Analyzing Journals using Scopus
• Next: Google Scholar Metrics >>
• Last Updated: Feb 17, 2022 4:09 PM
• URL: https://osu.libguides.com/oardc/citation_analysis

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Measures of Impact for Journals, Articles, and Authors

Elizabeth m suelzer.

1 Medical College of Wisconsin, Milwaukee, WI USA

Jeffrey L. Jackson

2 Zablocki VA Medical Center, Milwaukee, WI USA

Journals and authors hope the work they do is important and influential. Over time, a number of measures have been developed to measure author and journal impact. These impact factor instruments are expanding and can be difficult to understand. The varying measures provide different perspectives and have varying strengths and weaknesses. A complete picture of impact for individual researchers and journals requires using multiple measures and does not fully capture all aspects of influence. There are only a few players in the scholarly publishing world that collect data on article citations: Clarivate Analytics, Elsevier, and Google Scholar (Table ​ (Table1). 1 ). Measures of influence for authors and journals based on article citations use one of these sources and may vary slightly because of differing journal coverage.

Citation Databases

Individual Authors

Researchers make contributions to their fields in many ways: through education, advocacy, mentorship, collaboration, reviewing grants and articles, editorial activities, and leadership. For better or worse, their impact is usually based on the number of research articles they publish and how often those articles are cited. Some activities, such as writing editorials for leading journals, book chapters, or other clinical texts; testifying before Congress; or helping to shape government or health system policy, can be highly influential, but not credited in these measures of influence.

A common problem authors have in determining their impact is duplicate names, either from being inconsistent in the name they use (e.g., Jackson JL vs Jackson J) or name changes. There are several ways to establish a persistent and unique digital identifier. Researchers should take advantage of all.

ORCID ( www.orcid.org )

Many funders require an ORCID identifier as part of grant submission. ORCID is free, and all authors can sign up to create a unique identifier. ORCID does not track measures of impact, but cooperates with other sites that do by maintaining a list of publications that authors can review for completeness and accuracy.

ResearcherID ( www.researcherid.com )

This site provides a unique identifier and pulls information from Web of Science (Clarivate) to generate an h -index. It has a dashboard that generates a Web of Science author impact plot, provides authors a year-by-year report on impact, and generates a “citation” map that shows the location of citations. ResearcherID is also used by Publons, another Clarivate product, that tracks peer review and editorial activity. Access requires a subscription.

Scopus and Web of Science

Scopus and Web of Science are independent sites that create unique identifiers for authors based on proprietary software. Identifiers are automatically assigned and may result in the creation of more than one identifier, particularly if authors have had multiple affiliations, have a common name, have changed names, or have been inconsistent in their name. Authors can review the identifiers assigned and merge different listings. Access to these databases requires a subscription.

In addition, authors can create a Google Scholar account, which will also track and assess author impact. Google Scholar is free. Authors should regularly review their account to make sure their article list is accurate.

Measures of Impact for Authors

There are a number of different measures of individual author impact; each has strengths and weaknesses (Table ​ (Table2). 2 ). All are limited in that they do not account for author effort and order. Most can be skewed by self-citation and favor those who have been publishing longer. 2

Author Measures of Influence

H-Index , developed by Jorge E. Hirsch in 2005, is defined as the number of published papers that have been cited at least h times. 3 An h -index of 40 means th.e author has 40 articles cited at least 40 times. This simple metric is widely used for evaluating an authors’ impact. Citation databases like Web of Science, Scopus (Elsevier), and Google Scholar provide h -index information in their author profiles, though the reported h -index may vary due to citation coverage. The h -index favors authors that publish a continuous stream of papers with persistent, above-average impact. It measures the cumulative impact of an author’s work and combines quantity and quality. However, it does not account for the author effort and order, is biased against early-career researchers with fewer publications, and can be skewed by self-citation.

G-Index , created in 2006 by Leo Egghe, is defined as the largest number such that the top “ g ” articles received together at least g 2 citations. 4 This metric favors highly cited articles; a single highly cited article will increase the g -index considerably, while only increasing the h -index by 1.

i-10-Index , calculated by Google Scholar, is a straightforward metric that shows the number of publications with at least 10 citations.

Measures of Impact for Individual Articles

This is an NIH dashboard of bibliometrics for articles. iCite has three modules: Influence, Translation, and Open Citations. Influence is based on a relative citation ratio (RCR), comparing article citations to the median for NIH–funded publications, the value of which is set at 1.0. Among NIH–funded studies, the 90 th percentile for RCR is 3.81. Among all studies, the 90 th percentile is 2.24. Individual paper influence is reported and can be used to select manuscripts that best represent one’s work. Translation provides a measure of translation from bench to bedside by breaking down whether most of the author’s publications are molecular/cellular, animal, or human. Citations provide a count of the total citations and give citation statistics (mean, median, SE, maximum) as well as a list of the citing articles for each paper.

Alternative measures of influence

There are measures of influence of individual articles that are not based on citations. They provide a snapshot of article impact in a number of alternate venues, such as public policy documents, news articles, blogs, and social media.

Altmetric tracks more than 15 different sources, including public policy documents, news articles, blog posts, mentions in syllabi, reference managers, and social networks, such as Twitter and Facebook. The results are weighted; some sources, such as news articles, get greater weight. For example, in 2020, the weights of the various sources were news stories: 8, blogs: 5, Q&A forums: 2.5, Twitter: 1, Google: 1, and Facebook: 0.25. Altmetrics can be displayed as a “badge,” a symbol with a number in the middle of a circle with the strands colored to reflect the elements that went into the score. Researchers can sign up to create an altmetric badge for their articles ( www.altmetric.com ). To create a badge, the article must have a DOI number. Altmetrics for any specific article reflects popular interest in the topic rather than scientific importance. At JGIM, article altmetrics do not correlate with citations. Altmetrics can accumulate quickly; many metrics, such as Twitter and Facebook mentions, tend to occur within days of publication, while citations can take years. Altmetrics can be applied to scholarly products other than research publications, such as curricula and software. However, altmetrics can be gamed; “popular” topics tend to get more play than others. It is still unclear how to use altmetrics; most rank and tenure committees do not include these measures in promotion deliberations.

PlumX Analytics

PlumX gathers metrics into 5 categories: citations, usage, captures, mentions, and social media. Citations include traditional citations as well as ones that may have societal impact, such as policy documents. Usage measures views, downloads, and measures of how often the article is read. Captures indicate that a reader is planning on coming back to the article; it can indicate future citations. Mentions refer to news articles, blog posts, and other public mentions of the paper. PlumX Social Media refers to tweets and Facebook likes and shares, among several sources. It provides a picture of how much public attention articles are getting. PlumX analytics suffer from the same issues as altmetrics and citations. PlumX analytics are embedded in several platforms, including Mendeley, Science Direct, and Scopus and on many open-access journal platforms.

Measures of Impact for Journals

Historically, there were many reasons why certain journals rose to the top: highly respected editors, a long publishing history, and a track record of influential work policy makers and clinicians cared about. In 1975, Thompson Reuters debuted SCI Journal Citation Reports , ranking journals based on article citations. 5 Subsequently, this has been the primary basis for journal prestige.

Journal evaluation metrics that use citation data favor some disciplines over others. Disciplines vary widely in the amount of research output, the number of citations that are normally included in papers, and the tendency of a discipline to cite recent articles. 6 For example, Acta Poetica focuses on literary criticism. Its impact factor would be a poor measure of the journal’s influence. In addition, one needs to consider where the evaluation tool is collecting their data. Databases like Web of Science and Scopus may have stronger coverage of some disciplines, impacting the citation metrics that are generated. 6

Some resources assign journals to subject categories, making it possible to compare journals within their discipline. A good analogy is points scored in sporting events. Seven points in American football is a poor offensive outing, while 7 points in European football is a juggernaut. Comparing journals within the same discipline provides better information about the journal’s relative importance.

Journal Citation Reports

Journal Impact Factor (JIF). This is published annually by Clarivate and uses citation data from Web of Science. It has been the “gold standard” for measuring journal impact since its creation. 7 Journal editors nervously await release of their impact factor every summer. The JIF is calculated by dividing the total number of citations in the previous 2 years by the number of “source” articles published the following year. JGIM had 2810 citations in 2020 for articles published in 2018 and 2019; 548 of these articles were categorized as source material. Dividing 2810/548 yields our 2020 impact factor of 5.128. Not everything journals publish is considered source material. Clarivate does not provide guidance to journals on how they decide what types of material to count. In general, letters and editorials are not included. JGIM falls in the Medicine, General & Internal and the Health Care Sciences & Services categories, ranking 27 th and 11 th , respectively, in each. Seeking high JIF has led some journals to reduce the number of articles they publish, increase the amount of non-source papers, and focus on work they believe will be highly cited. The JIF is also susceptible to journal self-citation.

Journal Citation Indicator (JCI) is a normalized metric that debuted in 2021; a score of 1.0 means that journal articles were cited on average the same as other journals in that category. 8 JGIM has a JCI of 1.48 (Table ​ (Table3), 3 ), meaning we have a 48% more citation impact than other journals in our category. Based on the JCI, JGIM ranks 23 rd in Medicine, General & Internal and 15 th in Health Care Sciences & Services.

Journal Measures of Impact

* Source articles: articles that are counted in the denominator

5-Year Impact Factor is the average number of times articles published in the previous 5 years were cited in the indexed year. It gives information on the sustained influence of journal publications. JGIM’s 2020 score was 6.070, meaning that articles published in 2014–2019 were cited an average of 6 times in 2020.

Immediacy Index is the number of citations that occur in the year of publication. Journals with high immediacy index scores are rapidly cited. JGIM has a score of 1.861. This measure has been criticized for penalizing articles published later in the year.

Eigenfactor Score , a metric created in 2007 by Carl Bergstrom and Jevin West of the University of Washington, is based on the number of times articles from a journal over the past 5 years have been cited in the indexed year and gives citations in highly cited journals more weight than lesser cited ones. Self-citations by the journal are excluded. JGIM’s 2020 eigenfactor score was 0.02895. This measure suffers from being difficult to understand.

The Normalized Eigenfactor Score provides a normalized metric of the Eigenfactor Score, setting a score of 1 as the average for all journals. Like the Eigenfactor Score, citations that come from highly cited journals carry more weight than citations from less cited journals and journal self-citations are excluded. JGIM’s score is 6.07, meaning that JGIM was sixfold more influential than the average journal in the Web of Science database.

Article Influence . This measure is calculated by dividing the Eigenfactor Score by the number of a journal’s articles over the first 5 years after publication. It is calculated by multiplying the Eigenfactor Score by 0.01 and dividing by the number of articles in the journal, then normalized as a fraction of all articles in all publications, such that the mean is 1.0. JGIM’s most recent influence score is 2.579. This indicates that JGIM is more than twice as influential as the average journal.

CiteScore is calculated by dividing the number of citations from documents (articles, reviews, conference papers, book chapters, and data papers) over the previous 4 years by the number of articles indexed in Scopus published by the journal during those years. JGIM’s CiteScore is 4.6. Cite scores are calculated on a monthly basis. Among 122 internal medicine journals, JGIM is ranked 40 th by the CiteScore.

SCImago Journal Rank (SJR) also uses Scopus data and weights citations according to the prestige of the citing journal, taking into account the thematic closeness of the citing and cited journals. 9 It is calculated based on citations in 1 year to articles published in the previous 3 years. JGIM’s SJR is 1.746, which puts us 13 th on the list of “internal medicine” journals.

SCImago H-Index calculates the number of journal articles ( h ) that have been cited at least h times. It is the same calculation used to evaluate authors; SCImago calculates the journal h -index using Scopus citation data. JGIM has an h -index of 180, meaning that 180 of our articles have been cited more than 180 times. The h -index measures the productivity and impact of journal publications.

Source Normalized Impact per Paper (SNIP) compares each journal’s citations per article with the citations expected in its field. It allows a comparison of the journal’s impact across fields, because it adjusts for the likelihood of journal articles in that field being cited. JGIM’s SNIP is 1.471 which ranks us as 23 rd among 112 internal medicine journals.

Google Scholar

H5-index. Google Scholar calculates an H5-index for journals, which is the number of articles in the last 5 years with at least h citations. Google Scholar classifies JGIM as a primary care health journal. JGIM has an H5-index of 65, making it the top-ranked journal in this category. Google Scholar does not make available the citation sources; consequently, it is difficult to tell how complete the data is.

Journal Altmetrics

Like individual articles, altmetrics can be generated for journals. They have the same advantages and disadvantages as individual article altmetrics. In 2020, JGIM had 2.5 million downloads, 61 k linkouts, and 33 k social media mentions. Journal editors may have a poor understanding of altmetrics and struggle to know what to do with the data. Altimetrics reflect popular interest. For example, in 2020, the COVID pandemic captured public interest; articles focused on aspects of the pandemic received considerable public attention. For JGIM, the top altimetric article examined the impact of masking on preventing the spread of COVID and had an altmetric score of 4829.

JGIM is interested in these measures to ensure that we (like our authors) are having an impact. However, we are not obsessed on these measures and will continue to put forward what feels most important and relevant for academic general internists.

Declarations

The authors had no conflicts of interest with this article.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Measuring Your Impact: Impact Factor, Citation Analysis, and other Metrics: Citation Analysis

• Measuring Your Impact

Citation Analysis

Find your h-index.

• Other Metrics/ Altmetrics
• Journal Impact Factor (IF)
• Selecting Publication Venues

About Citation Analysis

What is Citation Analysis?

The process whereby the impact or "quality" of an article is assessed by counting the number of times other authors mention it in their work.

Citation analysis invovles counting the number of times an article is cited by other works to measure the impact of a publicaton or author.  The caviat however, there is no single citation analysis tools that collects all publications and their cited references.  For a thorough analysis of the impact of an author or a publication, one needs to look in multiple databases to find all possible cited references. A number of resources are available at UIC  that identify cited works including: Web of Science, Scopus, Google Scholar, and other databases with limited citation data.

Citation Analysis - Why use it?

To find out how much impact a particular article or author has had, by showing which other authors cited the work within their own papers.  The H-Index is one specific method utilizing citation analysis to determine an individuals impact.

Web of Science

Web of Science provides citation counts for articles indexed within it.  It i ndexes over 10,000 journals in the arts, humanities,  sciences, and social sciences.

• Enter the name of the author in the top search box (e.g. Smith JT).  
• Select Author from the drop-down menu on the right.
• To ensure accuracy for popular names, enter Univ Illinois in the middle search box, then select “Address” from the field drop down menu on the right.  (You might have to add the second search box by clicking "add another field" before you enter the address)
• Click on Search
• a list of publications by that author name will appear.   To the right of each citation, the number of times the article has been cited will appear.   Click the number next to "times cited" to view the articles that have cited your article

Scopus provide citation counts for articles indexed within it (limited to article written in 1996 and after).   It indexes o ver 15,000 journals from over 4,000 international publishers across the disciplines.

• Once in Scopus, click on the Author search tab.
• Enter the name of the author in the search box.  If you are using initials for the first and/or middle name, be sure to enter periods after the initials (e.g. Smith J.T.). 
• To ensure accuracy if it is a popular name, you may enter University of Illinois in the affiliation field.  
• If more than one profile appears, click on your profile (or the profile of the person you are examining). 
• Once you click on the author's profile, a list of the publications will appear and to the right of each ctation, the number of times the article has been cited will appear.  
• Click the number to view the articles that have cited your article

 Dimensions (UIC does not subscribe but parts are free to use)

• Indexes over 28000 journals
• Does not display h-index in Dimensions but can calculate or if faculty, look in MyActivities
• Includes Altmetrics score
• Google Scholar

Google Scholar provides citation counts for articles found within Google Scholar.  Depending on the discipline and cited article, it may find more cited references than Web of Science or Scopus because overall, Google Scholar is indexing more journals and more publication types than other databases. Google Scholar is not specific about what is included in its tool but information is available on how Google obtains its content .   Limiting searches to only publications by a specific author name is complicated in Google Scholar.  Using Google Scholar Citations and creating your own profile will make it easy for you to create a list of publications included in Google Scholar.   Using your Google Scholar Citations account, you can see the citation counts for your publications and have GS calculate your h-index.  (You can also search Google Scholar by author name and the title of an article to retrieve citation information for a specific article.)

• Using your google (gmail) account, create a profile of all your articles captured in Google Scholar.  Follow the prompt on the scrren to set up your profile.   Once complete, this will show all the times the articles have been cited by other documents in Google Scholar and your h-index will be provided.  Its your choice whether you make your profile public or private but if you make it public, you can link to it from your own webpages.

Try Harzing's Publish or Perish Tool in order to more selectively examine published works by a specific author.

Databases containing limited citation counts:

• PubMed Central
• Science Direct
• SciFinder Scholar

About the H-index

The h-index is an index to quantify an individual’s scientific research output ( J.E. Hirsch )   The h-index is an index that attempts to measure both the scientific productivity and the apparent scientific impact of a scientist. The index is based on the set of the researcher's most cited papers and the number of citations that they have received in other people's publications ( Wikipedia )  A scientist has index h if h of [his/her] Np papers have at least h citations each, and the other (Np − h) papers have at most h citations each.

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Web of Science provides citation counts for articles indexed within it.  It indexes over 12,000 journals in the arts, humanities,  sciences, and social sciences.  To find an author's h-index in WOS:

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• If more than one profile appears, click on your profile (or the profile of the person you are examining).  Under the Research section, you will see the h-index listed.
• If you have worked at more than one place, your name may appear twice with 2 separate h-index ratings.  Select the check box next to each relevent profile, and click show documents.

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• Using your google (gmail) account, create a profile of all your articles captured in Google Scholar.  Follow the prompt on the screen to set up your profile.   Once complete, this will show all the times the articles have been cited by other documents in Google Scholar and your h-index will be provided.  Its your choice whether you make your profile public or private but if you make it public, you can link to it from your own webpages.
• See  Albert Einstein's
• Harzing’s Publish or Perish (POP) 
• Publish or Perish Searches Google Scholar.  After searching by your name, deselect from the list of articles retrieved those that you did not author.  Your h-index will appear at the top of the tool.  Note:This tool must be downloaded to use
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Journal Impact Factors

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According to Journal Citation Reports (JCR) , an  impact factor is a ratio focusing on original research. 

Impact factor = # of citations to all items published in that journal in the past two years (divided by) # of articles and reviews published over those past two years referencing those citations

For example, if a journal has an impact factor of 2.5, this means in the indexed year each article published was cited on average 2.5 times in the previous two years in that journal.

Impact factor is used for journals only.

JCR only includes  12,000 journals and conference proceedings from over 3,300 publishers.

• InCitesTM: Journal Citation Reports® This Web of Science hosted database is a citation-based research evaluation tool for journal performance metrics with the goal of offering a systematic and objective means to evaluate the journals based on citation data.
• Tips for Using JCR Tips for using the Web of Science InCitesTM Journal Citation Reports

Metrics on the Web

• Eigenfactor® Project
• Google Scholar - Metrics
• InCitesTM: Journal Citation Reports®
• PlumX Metrics

Research Guides 

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Impact Metrics : Journal Impact Factor (JIF)

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• Field Normalized Citation Metrics
• SCImago Journal Rank (SJR)
• Eigenfactor Score
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How are Journal Impact Factors typically used?

JIFs are a journal level metric .

How are Journal Impact Factors calculated?

JIFs are calculated by taking the number of citations in a given year to articles published in the journal within the 2 preceding years, divided by the total number of citable publications published in the journal within the 2 preceding years.

Note: JIFs are only available for journals indexed in Web of Science. Additionally, JIFs will be based only on citing publications in Web of Science. (i.e., citations from publications that are not in Web of Science will not be factored into JIFs).

How do I read a Journal Impact Factor?

For example, The Journal of Excellent Examples (JEE) has a JIF of 3 in 2020. This would mean that articles published in the JGE in 2018 and 2019 were cited an average of 3 times each in 2020.

Where can I find a journal's Journal Impact Factor?

JIFs are created by Journal Citation Reports (JCR). You can find JIFs in both JCR and Web of Science .

• Go to Journal Citation Reports
• Enter the journal name into the search bar
• If the journal has a unique name you might be automatically taken to the journal's JCR page. If not, look for the journal on your results page, and then click the title of the journal to go to the journal's JCR page
• Scroll down until you see the heading "Journal Impact Factor"
• The Journal Impact Factor (JIF) should display on the left

• Go to Web of Science
• From the dropdown to the left of the search bar, select "Publication Title"
• Enter the name of the journal into the search bar
• Look for the journal in your results list
• Click the name of the journal in one of the citations
• The Journal Impact Factor should appear on the left

What are some limitations of Journal Impact Factors as a metric?

Like any impact metric, JIFs have their limitations. Some of these limitations include:

• Largely limited to English and U.S. based journals
• Does not factor in journal productivity
• May be skewed by citation outliers (e.g., a single article may receive the vast majority of citations)
• Not all citations are "good" citations (e.g., Article A may cite Article B to reject Article B's findings)
• Does not exclude self citations
• What qualifies as a "good" JIF differs by field
• Does not take into account social impact (e.g., an article trending on Twitter)
• Due to large dataset (and lack of transparency in article-level citation metrics), difficult to replicate
• Like all impact metrics, vulnerable to gamification (e.g., citation cartels )

Where can I learn more?

For an overview of JIFs and their limitations , see:

Garfield E. The history and meaning of the journal impact factor . JAMA . 2006;295(1):90-93. doi:10.1001/jama.295.1.90.

Laccourreye O, Maisonneuve H. The impact factor in peril? Eur Ann Otorhinolaryngol Head Neck Dis . 2019;136(1):1-2. doi:10.1016/j.anorl.2018.10.013.

Wouters P, Sugimoto CR, Larivière V, et al. Rethinking impact factors: Better ways to judge a journal . Nature . 2019;569(7758):621-623. doi:10.1038/d41586-019-01643-3.

• Murphy LS, Kraus CK, Lotfipour S, Gottlieb M, Langabeer JR 2nd, Langdorf MI. Measuring scholarly productivity: A primer for junior faculty. Part III: Understanding publication metrics . West J Emerg Med . 2018 Nov;19(6):1003-1011. doi: 10.5811/westjem.2018.9.38213. Epub 2018 Oct 18. PMID: 30429933; PMCID: PMC6225941.

For a succinct overview of JIFs and their limitations , see:

Journal impact factor . Metrics Toolkit. Accessed April 8, 2022. https://www.metrics-toolkit.org/metrics/journal_impact_factor/

• San Francisco declaration on research assessment . Declaration on Research Assessment (DORA). Accessed April 4, 2022.   https://sfdora.org/read/

For a look into how JIFs can be gamed , see:

Davis P. The emergence of a citation cartel . The Scholarly Kitchen blog. April 10, 2012. Accessed April 4, 2022. https://scholarlykitchen.sspnet.org/2012/04/10/emergence-of-a-citation-cartel/

Oransky I. Ten journals denied  2020 impact factors because of excessive self-citation or "citation stacking." Retraction Watch blog . June 30, 2021. Accessed April 8, 2022. https://retractionwatch.com/2021/06/30/ten-journals-denied-2020-impact-factors-because-of-excessive-self-citation-or-citation-stacking/

Falagas ME, Alexiou VG. The top-ten in journal impact factor manipulation . Arch Immunol Ther Exp (Warsz) . 2008;56(4):223-226. doi:10.1007/s00005-008-0024-5.

• Kojaku S, Livan G, Masuda N. Detecting anomalous citation groups in journal networks . Scientific Reports . 2021 Jul;11(1). doi:10.1038/s41598-021-93572-3.
• << Previous: Journal Level Metrics
• Next: SCImago Journal Rank (SJR) >>
• Last Updated: Jan 30, 2024 9:15 AM
• URL: https://researchguides.library.wisc.edu/impact_metrics

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What is the impact factor, pros and cons of citescore and impact factor, alternatives to citescore and the impact factor, journal metrics comparison chart, article-level metrics.

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CiteScore : Metric in Scopus most closely related to Impact Factor. Citations received by all articles published in the last 4 complete years are divided by the number of articles published in the last 4 years.

SCImago Journal Rank (SJR) : Measures the scholarly influence of a journal by accounting for the number of citations as well as the prestige of the citing journals. SJR is based on the  eigenvector centrality measure  used in network theory. It is a size-independent measure that ranks journals based on their average prestige per article. 

Source Normalized Impact per Paper (SNIP) : Measures the contextual citation impact of a journal by weighting the citations based on the total number of citations in a discipline. This method normalized for differences in citation practices between disciplines, so that a single citation is given greater value where citations are less frequent in that field. 

Scopus also provides metrics for number of citations, number of documents, percentage of documents cited, and CiteScore rank (how the CiteScore for the journal compares to other journals in the same field). Explore all the metrics by searching the Sources list in Scopus .

The Impact Factor  is a long-standing metric commonly used to evaluate journals . It is an equation calculating the average citation frequency for a given journal over a given period of time. It is a ratio of citations to citable items. Generally speaking, the higher the number, the higher the quality and prestige of the journal, although the impact factor is most useful when evaluating journals within the same discipline. 

A/B = Impact Factor A = cites by all indexed articles in a given year to articles published in a specific journal in the two preceding years. B = total number of articles published by that journal in that time period.

The journal Impact Factor was invented in the 1960s by Eugene Garfield and was intended as a tool to help librarians make selection decisions and authors identify publishing venues. Today, the Impact Factor is a proprietary calculation that is available only through Thompson Reuters Journal Citation Reports. 

• Vetted, established metrics for measuring journal impact within a discipline
• Designed to eliminate bias based on journal size and frequency
• Individual articles makes an uneven contribution to overall metric.
• These metrics do not account for certain things, things like context (positive or negative citation) and intentionality (self-citation).
• The metrics are proprietary to and bound by the contents of their respective databases: Scopus for CiteScore and the Thomson Reuters database for Impact Factor. 
• Citations, on which the Impact Factor is based, count for  < 1% of an article's overall use . 

Eigenfactor : A measure of a journal's overall importance to the scientific community based on the origin of incoming citations over a period of time; citations from highly ranked journals are weighed more heavily. (Hosted by the University of Washington; built on Thomson Reuters bibliographic data.)

• Eigenfactor Journal Ranking

Journal Metrics : Publicly accessible metrics for journal evaluation that offer three alternative views of true citation impact of a journal. (Provided by Elsevier; built on Scopus bibliographic data.)

• SCImago Journal Rank (SJR) : Defined above.
• Source Normalized Impact per Paper (SNIP) : Defined above.
• Impact per Paper (IPP) : Measures the ratio of citations to citable items for a given journal over a given period of time. IPP is the most direct correlate to the Impact Factor, but it calculates this ration over three years rather than two and it includes only peer-reviewed scholarly papers in both the numerator and the denominator. IPP is the foundational metric for the SNIP. 

Article-level metrics include:

• Citation count (Available through many databases and often on the publisher website.)
• Field-weighted citation impact (Calculated ratio in Scopus of the article's citations compared to the average number of citations received by all similar articles over a three-year window. A value greater than 1 means the article is cited greater than average.)
• Citation percentile (Scopus percentile comparing an article's citation count to the number of citations received by documents of the same type, published around the same time, in the same field.)
• View and/or download count (Number of times an article has been viewed and/or downloaded. Available in Scopus and often on the publisher website.)
• Altmetric score (Compilation of alternative metrics such as media mentions and citations in policy documents. Available through Altmetric Explorer for Institutions and on publisher websites which use Altmetric badges.)
• PlumX metrics  (Compilation of alternative metrics such as social media mentions and Mendeley readers. Available in Scopus and on publisher websites which use PlumX.)
• Article-Level Metrics: A SPARC Primer Guide to understanding the basics of Article-Level Metrics that explores the definition, application, opportunities and challenges presented by ALMs.
• More information on altmetrics available on this resource guide
• << Previous: Home
• Next: Altmetrics >>
• Last Updated: Jul 2, 2024 9:42 AM
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Answered By: Laurissa Gann Last Updated: Jun 13, 2022     Views: 1103148

Impact Factors are used to measure the importance of a journal by calculating the number of times selected articles are cited within the last few years. The higher the impact factor, the more highly ranked the journal. It is one tool you can use to compare journals in a subject category.

During 2017, the Journal Citation Reports (JCR) database tracked all impact factors for 12,298 journals. The table below shows the number and percentage of journals that were assigned impact factors ranging from 0 to 10+. Of 12,298 journals, only 239 titles, or 1.9% of the journals tracked by JCR, have a 2017 impact factor of 10 or higher. The top 5% of journals have impact factors approximately equal to or greater than 6 (610 journals or 4.9% of the journals tracked by JCR). Approximately two-thirds of the journals tracked by JCR have a 2017 impact factor equal to or greater than 1.

Impact Factors are useful, but they should not be the only consideration when judging quality. Not all journals are tracked in the JCR database and, as a result, do not have impact factors. New journals must wait until they have a record of citations before even being considered for inclusion. The scientific worth of an individual article has nothing to do with the impact factor of a journal.

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• Recommended video: Insights from Nobel Laureates, for scientists everywhere: How important is a journal’s impact factor? By Peter Doherty
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• What are altmetrics and how do I look them up?
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Comments (2)

• In 2016, the NEJM says their impact factor is 59. Is this possible? Do journals have IF that are greater than 30 today? ... Response from the MD Anderson Librarian... Yes, the NEJM has a 2015 impact factor of 59.558. There are 25 journals tracked by Journal Citation Reports that have an impact factor of 30 or higher. by Yvette Schlussel on May 22, 2017
• There is a journal with IF 2.88. Is this good or bad journal? The higher the Impact Factor, the better the journal. The 2.88 means that on average, any article published in that journal will be cited 2.88 times. You would have to compare this journal to journals in the same field to determine how it compares. by darshil trivedi on Dec 26, 2017

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Where do I find the Impact Factor of a journal?

The Impact Factor is a measure of scientific influence of scholarly journals. It measures the average number of citations received in a particular year by papers published in the journal during the two preceding years and is produced by a publisher called Thomson Reuters. The Impact Factor can be found on the Journal home page of journals that have an Impact Factor. 

Please note: Not all journals have an Impact Factor.

Follow these steps to find the Impact Factor of a journal:

• Search for a journal using the  ‘Journal/book title’  field on the ScienceDirect homepage or browse journal titles by selecting ' Journals & Books ' in the top right corner.
• Click the journal title to navigate to the journal’s home page.
• The Impact Factor and Journal CiteScore are mentioned in the header on the right side of the page.

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Impact factors: What they are, where to find them, how to use them

• Introduction to impact factors
• Which UC Merced databases include impact factors?
• Video tutorials on impact factors
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Instruction & Outreach Librarian

More on Citation Metrics

Wikipedia has an excellent collection of articles on various types and aspects of citation metrics, including impact factors, H-indexing, citation analysis and more. Click the image below if you're interested in learning more.

This guide was created in support of the Fall 2022 UC Merced Library workshop, "Impact factors: What they are, where to find them, and how to use them." Here, we'll discuss impact factors with emphasis on their use in writing theses and dissertations for degree credit at UC Merced.

What is an impact factor?

The impact factor measures the number of times a journal article has been cited by researchers in a given year. It's used to measure the importance of a scholarly journal -- that is, its importance to the discipline or field its articles cover, and by extension, the researchers working in that discipline or field -- by measuring the number of times articles in that journal are cited.

• Generally, impact factors are the best way to determine a journal's relative importance in a particular field or discipline. Your own research will be more readily accepted if it's based on the top journals -- meaning, the journals with the highest impact factors -- in your field.
• Impact factors are not perfect, and can be gamed, so to speak. Many journals will attempt to increase their impact factors by requiring that authors whose work is accepted for publication include citations to articles published in those journals.

Creating the impact factor

• Journal impact factors are calculated on the total number of citable articles in the two most recent, previous years. So it's not possible to get a journal impact factor for the present year. In 2022, the most recent journal impact factors will have been calculated on 2020 and 2021.
• Because journal impact factors are calculated on two years of article citations, it's not possible to calculate an impact factor for new journals.
• An impact factor of 10 is an excellent impact factor and indicates that the journal is of major importance in a field or discipline.
• An impact factor of 3 is considered to be good.
• Average impact factors for most journals are less than 1. However, this doesn't indicate that a journal is of poor quality. It may be a journal that publishes research in a field that is not noted for research.
• Next: Which UC Merced databases include impact factors? >>
• Last Updated: Oct 20, 2022 10:28 AM
• URL: https://libguides.ucmerced.edu/impact-factors

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• Published: 09 April 2005

Impact factors and their significance; overrated or misused?

• C Scully 1 , 2 &
• H Lodge 3 , 4  

British Dental Journal volume  198 ,  pages 391–393 ( 2005 ) Cite this article

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37 Citations

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Metrics details

Impact factor (IF) is a useful tool to evaluate a scientific publication.

The impact of a publication on the community can bear little relationship to the impact factor.

This paper describes how IF is calculated and some of its uses, with misuses highlighted.

The IFs of the dental journals are presented.

The journal impact factor (IF) is in widespread use for the evaluation of research and researchers, and considerable controversy surrounds it. The concept behind the IF is citations , and the number of them. The IF is a useful tool for the evaluation of journals, but it must be used carefully. Considerations include the number of review papers, letters or other types of material published in a journal, variations between disciplines, and item-by-item impact. Perhaps the most important use of the IF is in the process of academic evaluation. The extent to which the IF is appropriate for the evaluation of the quality of a specific article or journal and particularly for the evaluation of individual and collective research achievements is highly debatable.

Introduction

It has always been difficult to judge the importance of scientific papers or journals, and the impact of these on users and customers is a very fickle concept. Impact factor (IF), a term familiar to most people in the dental, medical, and scientific academic communities, appeared about 30 or more years ago ( http://www.isinet.com/essays/journalcitationreports/7html/ ), 1 , 2 , 3 and has come to be regarded by some as of increasing importance, yet few fully understand its meaning or implications. Perhaps the most important use of the IF is as a useful tool for the evaluation of journals but, since it is also employed in some situations in the process of academic evaluation, considerable controversy surrounds it. The 'impact' of a publication on the community can bear little relationship to the 'impact factor'.

Definitions

The journal impact factor (IF) is published annually by the ISI (a private, profit-oriented commercial Philadelphia-based organisation which was formerly termed the Institute for Scientific Information; http://www.isinet.com ). Access to the ISI databank is not free of charge.

The IF is a measure applied to a journal and is a measure of the frequency with which the 'average article' has been cited in a particular year or period. The key idea behind the IF is citations, and the number of them. Citations are the papers and book chapters listed in the references at the end of a scientific paper. Journal citation patterns (ie which author was being cited and where) have been studied since the 1920s and, since the 1960s, the Science Citation Index (SCI) and the Journal Citation Reports (JCR) have been produced based upon computer-compiled statistical reports.

The citation index makes it feasible to produce computer-compiled statistical reports not only on the output of journals but also in terms of their citation frequency. The JCR provides quantitative tools for ranking, evaluating, categorising, and comparing journals. The JCR uses the IF, along with other criteria, to compare, evaluate and rank journals; it can also indicate the largest journals, the 'hottest' journals, what publications a journal cites and which publications cite the journal itself. The IF is calculated by dividing the number of current citations to articles published in a specific journal in the previous two-year period by the total number of articles published in the same journal in the corresponding two-year period ( Table 1 ). The annual JCR IF is a ratio between citations and recent citable items published.

The impact factors are published every September. So, in September 2003, the IF for a journal for 2002 would have been calculated from the number of citations in the year 2002 to articles published in the journal in 2000 and 2001, divided by the number of articles published in the journal in 2000 and 2001. The IF is thus calculated from the number of times the articles are cited divided by the number of articles in that journal that could be cited.

It should be noted that, when the IF is calculated, the numerator is the total number of citations to virtually any item in the journal, all types of articles, such as editorials, letters and abstracts from meetings are included in the numerator — the cited items. However, the denominator is the number of articles only and includes only normal articles, notes and reviews — the citable items. The IF can be affected, for example, by a large correspondence section or by publishing controversial editorials. If a journal publishes more additional items, they become included in the numerator and thus the impact factor may well increase significantly. In contrast, an increase in citable items can have the opposite effect. For example when, in 1997, the Lancet divided its 'Letters' section into 'Correspondence' and 'Research Letters' — the latter being peer-reviewed and hence 'citable' for the denominator, the increase in the denominator led to a fall in IF from about 17 to about 12.

Only journals covered by the SCI database are scanned for their reference lists, only a very small proportion (about 5,000 science and technology titles) of the world total of over 100,000 English language journals and chiefly those from the USA are favoured, and tend to enjoy higher IF than non-English language journals. Books and book chapters are not scanned for their bibliographies, or included in any IF calculation.

Where can impact factors be found?

Journals often advertise their IF on their websites and publicity fliers (eg http://www.elsevier.com/locate/oraloncology ). Otherwise, a university library or a library of one of the larger institutions can access them either on CD or on the Internet ( http://www.isinet.com/isi or http://wos.mimas.ac.uk ).

Impact factors of dentally-related journals

The impact factors for 2002 for the first 20 of the 49 dental journals evaluated in JCR are shown in Table 2 . The IFs ranged from 1.047 to 2.956, and no other journal in this field had an IF ≤1 Interestingly, two of the three journals with the highest IFs are review journals ie they contain essentially no original research.

Uses of impact factor

The IF can be helpful to evaluate a journal's relative 'importance', especially when compared to others in the same field but, as noted above, this can be comparable to comparing apples with pears. The IF is useful in clarifying the significance of absolute (or total) citation frequencies. It eliminates some of the bias of such counts which favour larger journals over small ones, of older journals over newer ones or of frequently issued journals over those less frequently issued. However, larger journals have a larger citable body of literature than smaller or younger journals and, generally speaking, the larger the number of previously published articles, the more often a journal will be cited and the higher the IF will be.

Publishers often use IF for marketing (many fliers give the journal IF) or in identifying opportunities for new journal launches or taking decisions on whether to expand, merge or discontinue existing titles. Authors sometimes use IF to decide where to publish and to discover other journals in their specialty. Other uses of IF are discussed below.

Care in the use of impact factors

Informed and careful use of IF data is essential. Ill-formed conclusions based on IF statistics may arise unless several caveats are considered.

Criticisms levelled against IF have been well documented 4 and the main ones are summarised here.

The scientific field to which the journal belongs influences IF . The scientific field to which the journal belongs influences the IF greatly; ISI recognises this and warns against making comparisons between fields. For example, the highest impact factor in the ISI subject category 'Dentistry' is 2.956, whereas that in 'Oncology' is CA Cancer J Clin , with a massive IF of 32.886. But does that really mean that Oncology is 15 times as good or important as Dentistry? Different disciplines have widely differing citation practices, and general journals are at a particular advantage over more specialist journals.

Scientific journals generally rank higher than clinical journals . Scientific journals generally rank higher than clinical journals in the IF league, partly due to the fact that scientific papers tend to cite only scientific and not clinical articles, whereas clinical papers tend to cite both scientific and clinical articles.

Self-citation is also possible, increasing the IF . Self-citation is also possible, so authors happily cite previous papers in the same journal and editors may cite editorials. Some journals have been known to try to manipulate the IF by writing to authors asking them to add references to articles published in that journal.

Errors, misprints and inconsistencies in citations can distort the IF . Errors, misprints and inconsistencies in citations can distort the IF, and cause damage. Seglen 4 suggests that misprints in lists of references may affect up to one-quarter of references. Errors in the published IF have caused considerable damage to at least two journals in the dental field.

IFs are biased toward journals that mainly publish review articles . IFs are biased toward journals that are review journals or mainly publish review articles, since those tend to be cited more frequently — often in authors' introductions. 5 Amongst the dental journals with the highest IFs ( Table 2 ) are the review journals Critical Reviews in Oral Biology and Medicine , and Periodontology 2000 .

Multi-author and consortia articles sometimes pose a problem . Multi-author and consortia articles sometimes pose a problem regarding who — or what — should be cited, and how. For example, the International Human Genome Sequencing Consortium described the sequencing of the human genome in a paper in Nature in 2001 — probably the most important scientific advance ever and of phenomenal impact, but the IF was low. 6

Greater availability tends to raise the IF . Free electronic access, or the inclusion of a journal as part of the membership to a society and therefore greater availability, tends to raise the IF of a journal. Accessibility may mean that the source is cited but it does not necessarily follow that the most appropriate or 'best' reference has been chosen to be cited. For example, in the SCI subject category 'Dentistry', the journal with the highest impact factor ( Table 2 ) is the Journal of Dental Research . Apart from the high quality, the fact that J Dent Res is taken by most dental researchers, has surely helped it become the most highly cited journal.

Controversial or poor papers may increase the IF.7 It is worth remembering that a paper may be cited as an example of poor research or may be highly cited if it covers a controversial topic eg suggesting that HIV is not the cause of AIDS.

Citation counts in JCR do not distinguish between letters, reviews, or original research . So, if a journal publishes a large number of letters, there will usually be a temporary increase in citations of those letters. Letters to the Lancet may indeed be cited more often that letters to JAMA or vice versa, but the overall citation count and IF recorded would not take into account this artefact.

A change in journal title may adversely affect the IF . In the first year after a change, the IF is not available for the new title unless the data for old and new can be unified, and in the second year, the IF is split. The new title may rank lower than expected and the old title may rank higher than expected because only one year of source data are included in the calculation. For example, the journal Oral Oncology , once a daughter journal of the European Journal of Cancer , was undercited for several subsequent years, since many authors and journals were mistakenly still quoting it as Eur J Cancer rather than as Oral Oncology . Thus the IF for Eur J Cancer was artificially inflated and that for Oral Oncology was falsely low.

Contentious uses of impact factors

Citation analysis, in the hands of non-experts, can be an extremely blunt instrument. 8 Probably the most serious criticism of IFs relates to their erroneous and potentially dangerous use to determine 'author impact'. In some countries and institutions, academic administrators significantly (and controversially) use IF as a convenient tool in the process of deciding on promotion and tenure, ie the IFs of journals in which candidates have published are used to help determine the impact or importance of their research. IFs can represent an all too convenient shortcut to bypass proper appraisal, and the work involved in obtaining the more meaningful information of citation counts for individual articles and authors. By extension, and perhaps erroneously, IFs may be taken as an indication of a person's scientific worth. By further extension, there is a possibility of IFs being used to compare institutions.

However, it cannot be over-emphasised that the IF was created with the intent of comparing journals , not authors or individual articles. The IF can be used to provide a gross approximation of the prestige of journals in which individuals have published, but this is best done in conjunction with other considerations such as peer review, productivity, and subject specialty citation rates.

Nevertheless, despite these problems, many bodies responsible for hiring, promotion, tenure, or the assessment of research groups and grant proposals for funding and resource allocation continue to evaluate the quality of an individual's research and publications by looking at the IF of the journals in which they have published. The widespread belief that the IF is representative of an individual author (or article) has been countered by the finding that citations of individual articles in a journal show a very skewed distribution. 4

Authors consequently feel significant pressure to submit papers to a journal with a high IF, whether or not that journal is the most appropriate platform for their work. Many believe that the higher the journal IF that published their paper, the more their paper will be cited, but this is a myth. 4

Conclusions

The impact factor is a useful tool for evaluation of journals, but it must be used very carefully. Considerations include the amount of review articles, letters or other types of material published in the journal, variations between disciplines, and item-by-item impact.

The IF is of questionable value in certain circumstances and the extent to which the IF of a journal is appropriate for the evaluation of the quality of a specific article or journal and particularly of individual and collective research achievements is undoubtedly highly questionable. 9 , 10 , 11 , 12 , 13 , 14 Certainly, the actual impact on the community of an article is not necessarily related to the IF.

Garfield E . Citation analysis as a tool in journal evaluation. Science 1972; 178 : 471– 479.

Article   Google Scholar  

Garfield E . 'Citations to' divided by 'items published' gives journal impact factor. Essays of an Information Scientist. Current Contents 1972; 1 : 270– 273.

Google Scholar  

Garfield E . Which medical journals have the greatest impact? Ann Intern Med 1986; 105 : 313– 320.

Seglen P O . Why the impact factor of journals should not be used for evaluating research. BMJ 1997; 314 : 498– 502.

Krauze T K, Hillinger C . Citations, references and the growth of scientific literature: a model of dynamic interaction. J Am Soc Info Sci. 1971; 22 : 333– 336.

Human genomes: public and private. Nature 2001; 409 : 745 (editorial).

Moed H F, Van Leeuwen T N V . Improving the accuracy of Institute for Scientific Information's journal impact factors. J Am Soc Info Sci 1995; 46 : 461– 467.

Adam D . The counting house. Nature 2002; 415 : 726– 729.

Saper C B . What's in a citation impact factor? A journal by any other measure. J Comp Neurol 1999; 411 : 1– 2.

Bloch S, Walter C . The Impact Factor: time for change. Aust N Z J Psychiatry 2001; 35 : 563– 568.

Chew F S, Relyea-Chew A . How research becomes knowledge in radiology: an analysis of citations to published papers. Am J Roentgenol 1988; 150 : 31– 37.

Garfield E . Citation indexing for studying science. Nature 1970; 227 : 669– 671.

Kaltenborn K F, Kuhn K . [The journal impact factor as a parameter for the evaluation of researchers and research] Med Klin (Munich) 2003; 98 : 153– 169.

Talamanca A . The impact factor in the evaluation of research. Bull Group Int Rech Sci Stomatol Odontol 2002; 44 : 2– 9.

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Scully, C., Lodge, H. Impact factors and their significance; overrated or misused?. Br Dent J 198 , 391–393 (2005). https://doi.org/10.1038/sj.bdj.4812185

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Published : 09 April 2005

Issue Date : 09 April 2005

DOI : https://doi.org/10.1038/sj.bdj.4812185

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Impact factor

What is an impact factor?

The impact factor (IF) of a journal is a description of the influence the journal has in academic or university research circles. It is is a measure of how often the average research article in a journal has been cited or used in other research in any particular year.  The IF is used to measure the importance or rank of a journal by calculating the times it’s articles are cited. The higher the IF the more influential the journal.

Nature is the highest rated journal with an IF of 38.12

The Harvard Business Review has the lowest IF at 0.72

See also:  Harvard Business Review Impact factor

The Big difference between The Oxford Review and The Harvard Business Review

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How to avoid losing your Impact Factor

July 15, 2024

By Stefano Tonzani

© istockphoto.com/andreusK

Ten ways to ensure you don’t end up out in the cold

In 2023 more than 80 journals lost their Impact Factor. The reasons ranged from serious cases of ethical misconduct through to routine errors. Even small errors (such as presence of articles clearly outside the scope of the journal, or citations to sources not relevant to the context) can lead to losing an Impact Factor and wider de-indexation.

Journals can be flagged because of feedback from the community, or through tools developed by Clarivate (owner and operator of the Impact Factor). Clarivate uses 24 quality criteria in its flagging process opens in new tab/window . Some areas are flagged more often than others, however. The following guide illustrates the most common factors that lead to de-indexation and guides you through the simple steps that need to be taken to avoid such an eventuality.

1. Ensure there is quality control, editorial oversight & functioning peer review

Fraudulent or otherwise flawed research brings many problematic outcomes , including for journals. Elsevier is committed to upholding scientific ethics (for example by combating fake papers ) as part of delivering, together with its authors, editors and reviewers, a sizable share of the world’s highest quality research. A high level of editorial oversight, including rigorous peer review, should operate for all manuscripts submitted to a journal (including special issues and review articles). In addition, guest editors should be thoroughly vetted by the editorial team.

2. Stick to the stated scope and make sure articles abide by the guide for authors

Articles published which are clearly outside the stated aims & scope of the journal are a sign of poor editorial oversight and, as mentioned above, can lead to de-indexation.

Similarly, anything stated in the guide for authors should be reflected in practice. For example, if sharing a particular type of dataset is stated as being “required”, a process should be put in place to verify those datasets are indeed shared alongside their respective published articles.

3. Communicate changes and policies

Significant changes to the remit of the journal must always be carefully considered before being enacted. Even then, any substantial changes to important items such as the aims & scope can lead to loss of Impact Factor for a period of time, or a change in journal category. If the changes are significant, the journal could be subjected to a new editorial review by Clarivate. Therefore, such changes need to be proactively communicated to Clarivate by the publishing team. Furthermore, any procedure, policy or workflow adopted by the journal in its operations should be well-documented and easily defensible.

4. Be alert for manipulated or unjustifiable citations

Journals, authors and reviewers should not manipulate citations. Please refer to our previously published guidance for information and definitions on self-citations, citation stacking and citation rings.

Articles should not contain non-pertinent citations. These can be of two types: citations not pertinent to the topic of the article itself (e.g., citing an astrophysics reference in a cancer manuscript) and citations not pertinent to the context within the article where the citation is made (e.g., a reference about cancer genetics in a sentence discussing cancer biochemistry). These are often “throw-away” references placed in introductions. Any such citations should be removed.

5. Avoid conflicts of interest

Editors, guest editors, Editorial Board members, authors and reviewers should not serve in multiple roles in ways that can result in (a perceived) lack of independent judgement. For example, editors acting as reviewers on the same manuscripts. There are many other examples of conflicts of interest in peer review .

6. Check declaration of interest statements (and other ethical compliance items)

Indexation services require that the necessary compliance statements be present within every article. All journals require a declaration of interest statement; many fields require discipline specific items, such as patient consent forms. The presence and suitability of these must be checked by an editor at the manuscript triage stage.

7. Embrace diversity in all aspects of the journal

The journal's Publisher is responsible for appointing editors and the Editorial Advisory board, but they will often ask for suggestions of potential researchers to join. When making suggestions, help the Publisher by suggesting researchers who enhance the diversity of the team.

Too much content from one country (or worse from a handful of institutions) can be problematic, unless it can be justified according to the general field’s publication patterns. If your journal is imbalanced in terms of authorship, commissioning can lend a hand in boosting author numbers from hitherto unrepresented countries/institutions.

8. Act decisively on ethics cases

As an editor, you may become aware of a potential ethical problem with a published article. First, alert the journal Publisher; they will guide you through the process and clarify what is needed. Act quickly and decisively to resolve ethics cases in a timely manner. If an article should be retracted, authorize the retraction.

9. Scrutinize authorship changes

Authorship changes during revision are a frequently used tactic employed by paper mills. Authorship changes are permissible when accompanied by a significant revision of the paper (e.g., more experiments) and an explanation from the corresponding author in the cover letter. Authorship change requests that cannot be robustly defended should be declined.

10. Check for paper mill submissions

There are several hallmarks of paper mill activity . Editors should familiarize themselves with these papermill hallmarks and check manuscripts at the triage stage. Use an ethical misconduct rejection term to flag and reject such instances. There are also additional ways to combat paper mill activity .

Trust is the main currency of science publishing

All of these recommendations are good editorial practices, which point to the need for trust and transparency in all aspects of a journal’s peer review and publication activity. Building trust is a way to be proactive, rather than reactive on scientific ethics. Your publishing team is there to help you navigate these issues with best practices and specific advice.

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Stefano Tonzani

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Research on the impact of digital green finance on agricultural green total factor productivity: evidence from china, 1. introduction, 2. theoretical framework and research hypothesis, 2.1. theoretical framework of dgf on agtfp, 2.2. the direct effect of dgf on agtfp, 2.3. the impact mechanism of dgf on agtfp, 2.4. spatial heterogeneity of dgf on agtfp, 2.5. spatial spillover effects of dgf on agtfp, 3. research design, 3.1. methods, 3.1.1. panel data model, 3.1.2. quantile model, 3.1.3. spatial econometric model, 3.2. measurement of main variables, 3.2.1. measurement of dgf, 3.2.2. measurement of agtfp, 3.2.3. control variables, 3.3. data source and descriptive statistics, 4. empirical results and analysis, 4.1. baseline effect analysis, 4.1.1. baseline regression results, 4.1.2. impact mechanism analysis, 4.2. heterogeneity analysis, 4.3. spatial spillover effect analysis, 4.3.1. spatial autocorrelation analysis, 4.3.2. selection of spatial econometric models, 4.3.3. spatial regression results analysis, 4.4. robustness test, 5. discussion, 6. conclusions and policy implications, 6.1. conclusions, 6.2. policy implications, 7. limitations, author contributions, institutional review board statement, data availability statement, acknowledgments, conflicts of interest.

• Yu, X. Promoting Agriculture Green Development to realize the great rejuvenation of the Chinese nation. Front. Agric. Sci. Eng. 2020 , 7 , 119–120. [ Google Scholar ] [ CrossRef ]
• Wang, B.; Liu, G. Energy Conservation and Emission Reduction and China’s Green Economic Growth—Based on a Total Factor Productivity Perspective. China Ind. Econ. 2015 , 57–69. [ Google Scholar ] [ CrossRef ]
• Fang, Y.; Shao, Z. Whether Green Finance Can Effectively Moderate the Green Technology Innovation Effect of Heterogeneous Environmental Regulation. Int. J. Environ. Res. Public Health 2022 , 19 , 3646. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, L.; Tang, J.; Tang, M.; Su, M.; Guo, L. Scale of operation, financial support, and agricultural green total factor productivity: Evidence from China. Int. J. Environ. Res. Public Health 2022 , 19 , 9043. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jiakui, C.; Abbas, J.; Najam, H.; Liu, J.; Abbas, J. Green technological innovation, green finance, and financial development and their role in green total factor productivity: Empirical insights from China. J. Clean. Prod. 2023 , 382 , 135131. [ Google Scholar ] [ CrossRef ]
• Cheng, Z.; Kai, Z.; Zhu, S. Does green finance regulation improve renewable energy utilization? Evidence from energy consumption efficiency. Renew. Energy 2023 , 208 , 63–75. [ Google Scholar ] [ CrossRef ]
• Lan, J.; Wei, Y.; Guo, J.; Li, Q.; Liu, Z. The effect of green finance on industrial pollution emissions: Evidence from China. Res. Policy 2023 , 80 , 103156. [ Google Scholar ] [ CrossRef ]
• Huang, Y.; Chen, C.; Lei, L.; Zhang, Y. Impacts of green finance on green innovation: A spatial and nonlinear perspective. J. Clean. Prod. 2022 , 365 , 132548. [ Google Scholar ] [ CrossRef ]
• Lee, C.-C.; Lee, C.-C. How does green finance affect green total factor productivity? Evidence from China. Energy Econ. 2022 , 107 , 105863. [ Google Scholar ] [ CrossRef ]
• Wang, H.; Cui, H.; Zhao, Q. Effect of green technology innovation on green total factor productivity in China: Evidence from spatial durbin model analysis. J. Clean. Prod. 2021 , 288 , 125624. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Lei, J.; Zhang, Y. A study on the sustainable relationship among the green finance, environment regulation and green-total-factor productivity in China. Sustainability 2021 , 13 , 11926. [ Google Scholar ] [ CrossRef ]
• Kong, Q.; Shen, C.; Li, R.; Wong, Z. High-speed railway opening and urban green productivity in the post-COVID-19: Evidence from green finance. Glob. Financ. J. 2021 , 49 , 100645. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, H.; Lin, Q.; Wang, Y.; Mao, S. Can Digital Finance Improve China’s Agricultural Green Total Factor Productivity? Agriculture 2023 , 13 , 1429. [ Google Scholar ] [ CrossRef ]
• Ren, X.; Zeng, G.; Gozgor, G. How does digital finance affect industrial structure upgrading? Evidence from Chinese prefecture-level cities. J. Environ. Manag. 2023 , 330 , 117125. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, X.; Shao, X.; Chang, T.; Albu, L.L. Does digital finance promote the green innovation of China’s listed companies? Energy Econ. 2022 , 114 , 106254. [ Google Scholar ] [ CrossRef ]
• Lin, B.; Ma, R. How does digital finance influence green technology innovation in China? Evidence from the financing constraints perspective. J. Environ. Manag. 2022 , 320 , 115833. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sun, Y.; Tang, X. The impact of digital inclusive finance on sustainable economic growth in China. Financ. Res. Lett. 2022 , 50 , 103234. [ Google Scholar ] [ CrossRef ]
• Hu, J. Synergistic effect of pollution reduction and carbon emission mitigation in the digital economy. J. Environ. Manag. 2023 , 337 , 117755. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Du, M.; Hou, Y.; Zhou, Q.; Ren, S. Going green in China: How does digital finance affect environmental pollution? Mechanism discussion and empirical test. Environ. Sci. Pollut. Res. 2022 , 29 , 89996–90010. [ Google Scholar ] [ CrossRef ]
• Ozili, P.K. Digital finance, green finance and social finance: Is there a link? Financ. Internet Q. 2021 , 17 , 1–7. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Luan, L.; Wu, W.; Zhang, Z.; Hsu, Y. Can digital financial inclusion promote China’s economic growth? Int. Rev. Financ. Anal. 2021 , 78 , 101889. [ Google Scholar ] [ CrossRef ]
• Ahmad, M.; Majeed, A.; Khan, M.A.; Sohaib, M.; Shehzad, K. Digital financial inclusion and economic growth: Provincial data analysis of China. China Econ. J. 2021 , 14 , 291–310. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Chen, L. The impact of digital finance on green innovation: Resource effect and information effect. Environ. Sci. Pollut. Res. 2022 , 29 , 86771–86795. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yang, C.; Masron, T.A. Impact of digital finance on energy efficiency in the context of green sustainable development. Sustainability 2022 , 14 , 11250. [ Google Scholar ] [ CrossRef ]
• Cao, S.; Nie, L.; Sun, H.; Sun, W.; Taghizadeh-Hesary, F. Digital finance, green technological innovation and energy-environmental performance: Evidence from China’s regional economies. J. Clean. Prod. 2021 , 327 , 129458. [ Google Scholar ] [ CrossRef ]
• Qin, L.; Liu, S.; Wang, Y.; Gu, H.; Shen, T. Spatial coupling coordination and interactive response between green finance and green total factor productivity: Geographical analysis based on Chinese provinces, 2010–2020. Environ. Sci. Pollut. Res. 2024 , 31 , 20001–20016. [ Google Scholar ] [ CrossRef ]
• Laurett, R.; Paço, A.; Mainardes, E.W. Antecedents and consequences of sustainable development in agriculture and the moderator role of the barriers: Proposal and test of a structural model. J. Rural Stud. 2021 , 86 , 270–281. [ Google Scholar ] [ CrossRef ]
• Liu, D.; Zhu, X.; Wang, Y. China’s agricultural green total factor productivity based on carbon emission: An analysis of evolution trend and influencing factors. J. Clean. Prod. 2021 , 278 , 123692. [ Google Scholar ] [ CrossRef ]
• Sheng, Y.; Tian, X.; Qiao, W.; Peng, C. Measuring agricultural total factor productivity in China: Pattern and drivers over the period of 1978–2016. Aust. J. Agric. Resour. Econ. 2020 , 64 , 82–103. [ Google Scholar ] [ CrossRef ]
• Liu, J.; Dong, C.; Liu, S.; Rahman, S.; Sriboonchitta, S. Sources of total-factor productivity and efficiency changes in China’s agriculture. Agriculture 2020 , 10 , 279. [ Google Scholar ] [ CrossRef ]
• Qin, L.; Liu, S.; Hou, Y.; Zhang, Y.; Wu, D.; Yan, D. The spatial spillover effect and mediating effect of green credit on agricultural carbon emissions: Evidence from China. Front. Earth Sci. 2023 , 10 , 1037776. [ Google Scholar ] [ CrossRef ]
• Guo, J.; Zhang, K.; Liu, K. Exploring the mechanism of the impact of green finance and digital economy on China’s green total factor productivity. Int. J. Environ. Res. Public Health 2022 , 19 , 16303. [ Google Scholar ] [ CrossRef ]
• Li, G.; Jia, X.; Khan, A.A.; Khan, S.U.; Ali, M.A.S.; Luo, J. Does green finance promote agricultural green total factor productivity? Considering green credit, green investment, green securities, and carbon finance in China. Environ. Sci. Pollut. Res. 2023 , 30 , 36663–36679. [ Google Scholar ] [ CrossRef ]
• Hu, Y.; Liu, C.; Peng, J. Financial inclusion and agricultural total factor productivity growth in China. Econ. Model. 2021 , 96 , 68–82. [ Google Scholar ] [ CrossRef ]
• Shen, Z.; Wang, S.; Boussemart, J.-P.; Hao, Y. Digital transition and green growth in Chinese agriculture. Technol. Forecast. Soc. Change 2022 , 181 , 121742. [ Google Scholar ] [ CrossRef ]
• Chen, T.; Rizwan, M.; Abbas, A. Exploring the role of agricultural services in production efficiency in Chinese agriculture: A case of the socialized agricultural service system. Land 2022 , 11 , 347. [ Google Scholar ] [ CrossRef ]
• Zhou, X.; Chen, T.; Zhang, B. Research on the impact of digital agriculture development on agricultural green total factor productivity. Land 2023 , 12 , 195. [ Google Scholar ] [ CrossRef ]
• Liu, X.; Wang, X.; Yu, W. Opportunity or Challenge? Research on the Influence of Digital Finance on Digital Transformation of Agribusiness. Sustainability 2023 , 15 , 1072. [ Google Scholar ] [ CrossRef ]
• Zhu, Y.; Zhang, J. Can Green Finance Contribute to the Construction of Rural Ecological Civilization? J. Southwest Univ. Soc. Sci. Ed. 2023 , 49 , 103–115. [ Google Scholar ] [ CrossRef ]
• Wen, T.; He, Q. Pushing Forward Rural Revitalization on All Fronts and Deepening Rural Financial Reform and Innovation: The Logical Conversion, Breakthroughs and Path Selection. Chin. Rural Econ. 2023 , 93–114. [ Google Scholar ] [ CrossRef ]
• Ding, Q.; Huang, J.; Chen, J. Does digital finance matter for corporate green investment? Evidence from heavily polluting industries in China. Energy Econ. 2023 , 117 , 106476. [ Google Scholar ] [ CrossRef ]
• Zhang, S.; Wu, Z.; Wang, Y.; Hao, Y. Fostering green development with green finance: An empirical study on the environmental effect of green credit policy in China. J. Environ. Manag. 2021 , 296 , 113159. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zheng, W.; Zhang, L.; Hu, J. Green credit, carbon emission and high quality development of green economy in China. Energy Rep. 2022 , 8 , 12215–12226. [ Google Scholar ] [ CrossRef ]
• Lv, C.; Fan, J.; Lee, C.-C. Can green credit policies improve corporate green production efficiency? J. Clean. Prod. 2023 , 397 , 136573. [ Google Scholar ] [ CrossRef ]
• Hong, M.; Li, Z.; Drakeford, B. Do the green credit guidelines affect corporate green technology innovation? Empirical research from China. Int. J. Environ. Res. Public Health 2021 , 18 , 1682. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, M.; Liu, Y. Influence of digital finance and green technology innovation on China’s carbon emission efficiency: Empirical analysis based on spatial metrology. Sci. Total Environ. 2022 , 838 , 156463. [ Google Scholar ] [ CrossRef ]
• Xu, J.; She, S.; Gao, P.; Sun, Y. Role of green finance in resource efficiency and green economic growth. Resour. Policy 2023 , 81 , 103349. [ Google Scholar ] [ CrossRef ]
• Ouyang, H.; Guan, C.; Yu, B. Green finance, natural resources, and economic growth: Theory analysis and empirical research. Resour. Policy 2023 , 83 , 103604. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Zhou, H. Study on the Spatial Effect and Heterogeneity of Green Finance Development on the Transformation and Upgrading of Industrial Structure: Interpretation Based on spatial Durbin Model. J. Southwest Univ. Nat. Sci. Ed. 2023 , 45 , 164–174. [ Google Scholar ] [ CrossRef ]
• Ma, D.; Zhu, Q. Innovation in emerging economies: Research on the digital economy driving high-quality green development. J. Bus. Res. 2022 , 145 , 801–813. [ Google Scholar ] [ CrossRef ]
• Li, Z.; Chen, H.; Mo, B. Can digital finance promote urban innovation? Evidence from China. Borsa Istanb. Rev. 2023 , 23 , 285–296. [ Google Scholar ] [ CrossRef ]
• Chen, C.; Ye, A. Heterogeneous effects of ICT across multiple economic development in Chinese cities: A spatial quantile regression model. Sustainability 2021 , 13 , 954. [ Google Scholar ] [ CrossRef ]
• Lv, C.; Bian, B.; Lee, C.-C.; He, Z. Regional gap and the trend of green finance development in China. Energy Econ. 2021 , 102 , 105476. [ Google Scholar ] [ CrossRef ]
• Qiang, C.; Xu, W. Green Finance Have an Effect on the Economic High-Quality Development from the perspective of Space. Jianghan Trib. 2022 , 6 , 21–28. [ Google Scholar ] [ CrossRef ]
• Wang, B.; Wang, Y.; Cheng, X.; Wang, J. Green finance, energy structure, and environmental pollution: Evidence from a spatial econometric approach. Environ. Sci. Pollut. Res. 2023 , 30 , 72867–72883. [ Google Scholar ] [ CrossRef ]
• Lijun, M.; Ye, A. Influence and spatial spillover effects of the digital economy on the high-quality development of the tourism industry. Prog. Geogr. 2023 , 42 , 2296–2308. [ Google Scholar ]
• Li, T.; Lin, H. Regional Green Finance, Space Spillovers and High-quality Economic Development. Inq. Into Econ. Issues 2023 , 4 , 157–174. [ Google Scholar ]
• Xie, D.; Hu, S.; Bao, Y. Can Green Finance Improve China’s Urban Green Total Factor Productivity: Based on Data from 285 Cities in China. J. China Univ. Geosci. Soc. Sci. Ed. 2023 , 23 , 122–137. [ Google Scholar ] [ CrossRef ]
• Dong, R.; Wang, S.; Baloch, M.A. Do green finance and green innovation foster environmental sustainability in China? Evidence from a quantile autoregressive-distributed lag model. Environ. Dev. Sustain. 2023 , 1–23. [ Google Scholar ] [ CrossRef ]
• Xu, G.; Chang, H.; Yang, H.; Schwarz, P. The influence of finance on China’s green development: An empirical study based on quantile regression with province-level panel data. Environ. Sci. Pollut. Res. 2022 , 29 , 71033–71046. [ Google Scholar ] [ CrossRef ]
• Qin, L.; Liu, S.; Wang, Y.; Gu, H.; Shen, T. Regional differences, dynamic evolution, and spatial–temporal convergence of green finance development level in China. Environ. Sci. Pollut. Res. 2024 , 31 , 16342–16358. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Guo, F.; Wang, J.; Wang, F.; Kong, T.; Zhang, X.; Cheng, Z. Measuring China’s Digital Financial Inclusion: Index Compilation and Spatial Characteristics. China Econ. Q. 2020 , 19 , 1401–1418. [ Google Scholar ] [ CrossRef ]
• Shen, Y.; Guo, X.; Zhang, X. Digital financial inclusion, land transfer, and agricultural green total factor productivity. Sustainability 2023 , 15 , 6436. [ Google Scholar ] [ CrossRef ]
• Ma, J.; Meng, H.; Shao, D.; Zhu, Y. Green Finance, Inclusive Finance and Green Agriculture Development. Financ. Forum 2021 , 26 , 3–8+20. [ Google Scholar ] [ CrossRef ]
• Zhang, T. Can green finance policies affect corporate financing? Evidence from China’s green finance innovation and reform pilot zones. J. Clean. Prod. 2023 , 419 , 138289. [ Google Scholar ] [ CrossRef ]
• Li, X.; Liu, Y.; Song, T. Calculation of the Green Development Index. Soc. Sci. China 2014 , 6 , 69–95+207–208. [ Google Scholar ]
• Wang, F.; Du, L.; Tian, M. Does agricultural credit input promote agricultural green total factor productivity? Evidence from spatial panel data of 30 provinces in China. Int. J. Environ. Res. Public Health 2022 , 20 , 529. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yang, Y.; Ma, H.; Wu, G. Agricultural green total factor productivity under the distortion of the factor market in China. Sustainability 2022 , 14 , 9309. [ Google Scholar ] [ CrossRef ]
• Yin, Z.; Sun, X.; Xing, M. Research on the impact of green finance development on green total factor productivity. Stat. Decis. 2021 , 37 , 139–144. [ Google Scholar ]
• Gao, Q.; Cheng, C.; Sun, G.; Li, J. The impact of digital inclusive finance on agricultural green total factor productivity: Evidence from China. Front. Ecol. Evol. 2022 , 10 , 905644. [ Google Scholar ] [ CrossRef ]
• Fang, L.; Hu, R.; Mao, H.; Chen, S. How crop insurance influences agricultural green total factor productivity: Evidence from Chinese farmers. J. Clean. Prod. 2021 , 321 , 128977. [ Google Scholar ] [ CrossRef ]
• Zeng, Z.; Yan, J.; Zhang, D.L.; Liao, S.W. The Assistance of Digital Economy to the Revitalization of Rural China. In Proceedings of the 4th International Conference on Social Sciences and Economic Development (ICSSED), AEIC Acad Exchange Informat Ctr, Wuhan, China, 15–17 March 2019; pp. 702–704. [ Google Scholar ]
• Yu, H.J.; Zhu, Q. Impact and mechanism of digital economy on China’s carbon emissions: From the perspective of spatial heterogeneity. Environ. Sci. Pollut. Res. 2023 , 30 , 9642–9657. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yu, Z.; Liu, S.; Zhu, Z. Has the digital economy reduced carbon emissions?: Analysis based on panel data of 278 cities in China. Int. J. Environ. Res. Public Health 2022 , 19 , 11814. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yu, H.Y.; Wang, J.C.; Xu, J.J.; Ding, B.H. Does digital economy agglomeration promote green economy efficiency? A spatial spillover and spatial heterogeneity perspective. Environ. Dev. Sustain. 2024 . [ Google Scholar ] [ CrossRef ]

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Qin, L.; Zhang, Y.; Wang, Y.; Pan, X.; Xu, Z. Research on the Impact of Digital Green Finance on Agricultural Green Total Factor Productivity: Evidence from China. Agriculture 2024 , 14 , 1151. https://doi.org/10.3390/agriculture14071151

Qin L, Zhang Y, Wang Y, Pan X, Xu Z. Research on the Impact of Digital Green Finance on Agricultural Green Total Factor Productivity: Evidence from China. Agriculture . 2024; 14(7):1151. https://doi.org/10.3390/agriculture14071151

Qin, Lingui, Yan Zhang, Yige Wang, Xinning Pan, and Zhe Xu. 2024. "Research on the Impact of Digital Green Finance on Agricultural Green Total Factor Productivity: Evidence from China" Agriculture 14, no. 7: 1151. https://doi.org/10.3390/agriculture14071151

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• Published: 15 July 2024

Enhancing psychological well-being in college students: the mediating role of perceived social support and resilience in coping styles

• Shihong Dong 1 ,
• Huaiju Ge 1 ,
• Wenyu Su 1 ,
• Weimin Guan 1 ,
• Xinquan Li 1 ,
• Yan Liu 2 ,
• Qing Yu 1 ,
• Yuantao Qi 2 ,
• Huiqing Zhang 3 &
• Guifeng Ma 1  

BMC Psychology volume  12 , Article number:  393 ( 2024 ) Cite this article

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The prevalence of depression among college students is higher than that of the general population. Although a growing body of research suggests that depression in college students and their potential risk factors, few studies have focused on the correlation between depression and risk factors. This study aims to explore the mediating role of perceived social support and resilience in the relationship between trait coping styles and depression among college students.

A total of 1262 college students completed questionnaires including the Trait Coping Styles Questionnaire (TCSQ), the Patient Health Questionnaire-9 (PHQ-9), the Perceived Social Support Scale (PSSS), and the Resilience Scale-14 (RS-14). Common method bias tests and spearman were conducted, then regressions and bootstrap tests were used to examine the mediating effects.

In college students, there was a negative correlation between perceived control PC and depression, with a significant direct predictive effect on depression ( β = -0.067, P  < 0.01); in contrast, negative control NC showed the opposite relationship ( β  = 0.057, P  < 0.01). PC significantly positively predicted perceived social support ( β  = 0.575, P  < 0.01) and psychological resilience ( β  = 1.363, P  < 0.01); conversely, NC exerted a significant negative impact. Perceived social support could positively predict psychological resilience ( β  = 0.303, P  < 0.01), and both factors had a significant negative predictive effect on depression. Additionally, Perceived social support and resilience played a significant mediating role in the relationship between trait coping styles and depression among college students, with three mediating paths: PC/NC → perceived social support → depression among college students (-0.049/0.033), PC/NC→ resilience → depression among college students (-0.122/-0.021), and PC/NC → perceived social support → resilience → depression among college students (-0.016/0.026).

The results indicate that trait coping styles among college students not only directly predict lower depression but also indirectly influence them through perceived social support and resilience. This suggests that guiding students to confront and solve problems can alleviate their depression.

Peer Review reports

Introduction

Depression is a complex mental disorder, characterized by cognitive, affective and psychosocial symptoms [ 1 , 2 ]. It is projected that by 2030, depression will rank first globally in terms of years lived with disability [ 3 , 4 ]. Depression is also one of the most common mental health issues among contemporary college students [ 5 , 6 ]. Studies have shown that the detection rate of depression among Chinese college students ranges from 23–34% [ 7 , 8 ]. Compared to non-student populations, college students have a higher prevalence of depression, and this rate seems to be increasing [ 9 ]. This vulnerable group of college students is in a unique developmental stage, facing pressures not only from life but also from the demands of academic coursework and complex interpersonal relationships, making the factors influencing depression among college students, particularly complex [ 9 , 10 ].

Exploring the mechanisms by which influencing factors affect the occurrence of depression in college students is of significant importance for early prevention [ 11 ]. Research has demonstrated that trait coping style is one of the risk factors for depression among college students. Trait coping refers to the strategies individuals employ in challenging situations, categorized into positive coping and negative coping [ 12 , 13 ]. Positive coping focuses on taking effective action and changing stressful situations, typically associated with problem-solving behaviors and regulation of positive emotions, which can help reduce the incidence of depression [ 14 ]. Conversely, negative coping is a passive approach centered around negative evaluations and emotional expression, often involving avoiding problems and social isolation, which is more likely to lead to the development of depression [ 14 ]. Research indicates that positive coping strategies are inversely correlated with depression, serving as protective factors against depression. Conversely, negative coping strategies are positively associated with depression, acting as risk factors for its onset [ 15 ].

Perceived social support refers to an individual’s subjective emotional state of feeling supported and understood by family, friends, and other sources [ 16 , 17 ]. Prior studies have shown that perceived social support can directly impact an individual’s level of depression and also have indirect effects [ 18 ]. The data indicate that social support can significantly influence coping mechanisms, with groups having higher levels of social support tended to respond more actively and positively to stress from various sources [ 19 ]. Social support is considered an important mediating factor in determining the relationship between psychological stress and health, representing an emotional experience where individuals feel supported, respected, and understood [ 16 ]. The relationship between individuals’ coping strategies and depression may be influenced by the mediating role of perceived social support [ 20 , 21 ]. In addition to this, resilience plays a role in all three.Resilience refers to the ability to adapt to stress and adversity, enhancing an individual’s psychological well-being [ 22 ]. Both coping styles and perceived social support significantly predict resilience positively [ 23 ]. For individuals with strong resilience, possessing a high level of adaptive capacity can mitigate the negative effects of stress on individuals, thereby enhancing their mental health.

In recent years, there has been a growing body of research on the prevalence of depression among college students. However, the rates of depression vary in different environments, and there is limited research on the mechanisms through which trait coping styles, perceived social support, and resilience impact depression. Therefore, this study aims to investigate the mechanisms through which positive coping styles(PC), negative coping styles(NC), perceived social support, and resilience influence depression among college students. Additionally, it seeks to analyze the mediating roles of perceived social support and resilience in this context. The goal is to provide insights into the reasons behind depression among college students under different coping strategies, aiding in timely psychological adjustment to promote the comprehensive development of the mental and physical well-being of college students.

The following assumptions were made:

Hypothesis 1

PC has a significant negative predictive effect on depression among college students. NC has a significant positive predictive effect on depression among college students.

Hypothesis 2

Perceived social support serves as a mediator between PC/NC and depression among college students.

Hypothesis 3

Resilience mediates the relationship between PC/NC and depression among college students.

Hypothesis 4

Perceived social support and psychological resilience mediate the relationship between PC/NC and depression among college students in a serial manner.

Data and methods

This is a cross-sectional study that was conducted from January through February 2024. Using the Questionnaire Star network platform, we presented the questionnaire online, which was openly accessible to college students at a university in Shandong. The average time to complete the survey was 15 min. Participation was voluntary and students were informed about the purpose of the study. Confidentiality was assured and questionnaires were submitted anonymously. A total of 1267 enrolled college students participated in the questionnaire survey. After excluding invalid questionnaires, 1262 valid questionnaires were included, resulting in an effective rate of 99.57%.

Trait coping style questionnaire

The Trait Coping Style Questionnaire (TCSQ) [ 24 ], developed by Qianjin Jiang, was utilized to assess the trait coping styles of college students. This questionnaire reflects the participants’ approaches to coping with situations, comprising a total of 20 items. It consists of two dimensions: negative coping style and positive coping style, each with 10 items. Using a 5-point Likert scale ranging from “definitely not” to “definitely yes,” scores were assigned from 1.00 to 5.00. The Cronbach’s α coefficient for negative coping style was 0.906 and for positive coping style was 0.786 in this study.

Depression scale

The Patient Health Questionnaire-9 (PHQ-9) [ 25 ] was used to assess depressive symptoms in the past two weeks. This scale consists of 9 items rated on a 4-point Likert scale ranging from “not at all” to “nearly every day,” with scores from 0 to 3. The total score ranges from 0 to 27, with higher scores indicating more severe depressive symptoms. The Cronbach’s α coefficient for this scale in the current study was 0.884.

Perceived Social Support Scale

The Perception Social Support Scale (PSSS) was compiled by James A.Blumenthal in 1987 and later translated and modified by Qianjin Jiang to form the Chinese version of the Zimetm Perception Social Support Scale (PSSS) [ 26 , 27 ]. PSSS comprises 12 self-assessment items rated on a 7-point Likert scale. The scale includes three dimensions: family support (items 3, 4, 8, 11), friend support (items 6, 7, 9, 12), and other support (items 1, 2, 5, 10), with a total score ranging from 12 to 84. Scores of 12–36 indicate low support, 37–60 indicate moderate support, and 61–84 indicate high support. The Cronbach’s α for this scale in the current survey was 0.968.

Resilience scale

The Resilience Scale (RS-14) [ 28 ] Chinese version consists of 14 items, each rated on a 7-point Likert scale from “not at all” to “completely,” with scores ranging from 1 to 7. The total score ranges from 14 to 98, with higher scores indicating better resilience. The Cronbach’s α for this scale in the current study was 0.925.

Statistical analysis

Data were organized and analyzed using SPSS 26.0 software. Confirmatory factor analysis was first conducted on the questionnaires. Descriptive analysis was then performed on the scores of each scale. Spearman was used to examine the relationships between trait coping styles, perceived social support, resilience, and depression. Mediation analysis was carried out using the SPSS PROCESS macro 3.4.1 software model 6 developed by Hayes, specifically designed for testing complex models. Model 6 was applied for two mediating variables, followed by the bias-corrected percentile Bootstrap method with 5000 resamples to estimate the 95% confidence interval of the mediation effect. A significant mediation effect was indicated if the 95% confidence interval (CI) did not include zero. A significance level of P  < 0.05 was considered statistically significant.

Examination of common method bias

Systematic errors in indicator data results caused by the same data collection method or measurement environment can typically be assessed through the Harman single-factor test on 55 items in the dataset to examine common method bias. The results indicated that there were 7 factors with eigenvalues greater than 1, and the variance explained by the first factor was 34.84%, which was below the critical threshold of 40%. Therefore, this study may not have a significant common method bias.

Descriptive statistics and correlation analysis

The mean scores, standard deviations, and correlations of each variable are presented in Table  1 . PC ( r = -0.326, P  < 0.01), resilience ( r =-0.445, P  < 0.01), and perceived social support ( r =-0.405, P  < 0.01) were negatively correlated with depression. PC ( r  = 0.336, P  < 0.01) and resilience ( r  = 0.469, P  < 0.01) were significantly positively correlated with perceived social support. PC was significantly positively correlated with resilience( r  = 0.635, P  < 0.01). NC was significantly positively correlated with depression( r  = 0.322, P  < 0.01) and PC( r  = 0.146, P  < 0.01). NC was significantly negatively correlated with perceived social support ( r =-0.325, P  < 0.01).

Analysis of chain mediation effects

The chain mediation model was validated using SPSS PROCESS Model 6. Trait coping styles were considered as the independent variable, while depression among college students was treated as the dependent variable. Perceived social support and resilience were included as the mediating variables, culminating in the path model depicted in Figs.  1 and 2 .

The results of the regression analysis, as shown in Table  2 , indicated that PC could significantly predict perceived social support in a positive direction ( β  = 0.575, P  < 0.01). Both PC ( β  = 1.363, P  < 0.01) and perceived social support ( β  = 0.303, P  < 0.01) had significant positive predictive effects on psychological resilience. When simultaneously predicting depression using PC, perceived social support, and psychological resilience, all three exhibited significant negative predictive effects ( β = -0.067, β = -0.085, β = -0.090, P  < 0.01). NC could significantly predict perceived social support in a negative direction ( β = -0.457, P  < 0.01). When NC ( β  = 0.191, P  < 0.01) and perceived social support ( β  = 0.508, P  < 0.01) jointly predict psychological resilience, they both had significant positive predictive effects. When simultaneously predicting depression using NC, perceived social support, and psychological resilience, NC ( β  = 0.057, P  < 0.01) showed a significant positive predictive effect, while perceived social support ( β = -0.072, P  < 0.01) and psychological resilience ( β = -0.112, P  < 0.01) demonstrated significant negative predictive effects.

Further employing the Bootstrap sampling method, with 5000 repetitions, the significance of the mediating effects and chain mediation effects between trait coping styles and depression among college students was examined. The results indicated that the direct effects of PC/NC on depression were significant, with direct impact values of -0.067/0.057 (26.38%/60.00%). Perceived social support and psychological resilience mediated the relationship between PC/NC and depression, with this mediation encompassing three pathways: the separate mediating effect of perceived social support, with effect values of -0.049 and 0.033 respectively; the separate mediating effect of resilience, with effect values of -0.122 and − 0.021 respectively; and the serial mediating effect from perceived social support to resilience, with effect values of -0.016, -0.021, and 0.026. The 95% confidence intervals for all pathways did not include 0, indicating significant indirect effects. Therefore, the total indirect effects were − 0.187 (73.62%) and 0.038 (40.00%), showing that PC had a weaker direct effect on depression compared to NC, but a stronger indirect effect. This was illustrated in Table  3 .

Chain mediation model of perceived social support and resilience between PC and depression. ** p  < 0.01

Chain mediation model of perceived social support and resilience between NC and depression. ** p  < 0.01

Previous research on the associations and specific pathways among depressive symptoms, trait coping styles, perceived social support, and resilience in college students has been limited. Therefore, this study utilized a chain mediation model to examine how trait coping styles, perceived social support, and resilience influence depressive symptoms in college students. The results indicate that perceived social support and resilience not only act as separate mediators between PC/NC and depression but also exhibit a chain mediation effect.

Mechanisms of the impact of PC/NC on depression in college students

This study found that trait coping styles can significantly and negatively predict depressive symptoms in college students directly, consistent with previous research [ 29 ]. In recent years, amidst the backdrop of the pandemic, numerous studies have emerged domestically and internationally focusing on college students’ mental health from the perspective of crisis event coping [ 30 ]. These studies have predominantly concentrated on trait coping styles as a mediating variable in predicting the occurrence of depressive symptoms, with fewer studies examining the direct impact of trait coping styles on depressive symptoms. College students, being in a unique developmental stage, face challenges from various aspects and bear the pressures of academic coursework, interpersonal relationships, and future employment. Research indicates that trait coping styles are a key factor influencing mental health [ 31 ]. Implementing healthy coping techniques and interventions can help individuals overcome negative emotions caused by stress, which is an adaptive coping mechanism that assists college students in facing stress and enhancing problem-solving abilities, thus preventing or reducing the occurrence of depression. Conversely, adopting passive or avoidant coping strategies, leading to inadequate resolution of stress events, can increase psychological stress [ 14 ], thereby exerting a negative impact on the mental health of college students [ 32 ]. Therefore, trait coping styles play a negative predictive role in depressive symptoms among college students. PC was a positive predictor of depression and NC was a negative predictor of depression. This is consistent with previous studies [ 24 , 29 ].

Separate mediating effects of perceived social support and resilience

After introducing perceived social support and resilience as two mediating variables, the predictive effect of PC/NC on depressive symptoms in college students remained significant. The results show that PC can positively predict perceived social support, and NC is the opposite, consistent with previous research [ 33 ]. Trait coping styles are an important predictive factor in altering college students’ perceptions of social support and the occurrence of depression. Individuals who adopt negative coping styles tend to perceive relatively less external support. Some argue that social support plays a reverse predictive role in trait coping styles; the more social support college students receive and feel, the more likely they are to actively adopt positive coping strategies to alleviate stress, potentially due to variations in study subjects and time [ 34 ]. In this pathway, perceived social support can significantly and negatively predict depressive symptoms, aligning with previous research findings [ 35 ]. Perceived social support is considered a crucial mediating factor influencing mental health, referring to an individual’s ability to perceive support and understanding from family, friends, and others. College students with lower levels of perceived social support often feel neglected and undervalued, leading to negative evaluations and self-doubt, making them more susceptible to depression. PC/NC and perceived social support can interact and influence the occurrence of depressive symptoms in college students [ 16 ].

Research indicates that PC can significantly and positively predict resilience, with an indirect effect value of 48.03%.In this pathway, the mediating effect of resilience is more pronounced, consistent with previous studies [ 36 ]. There is a close connection between resilience and coping styles; college students who adopt positive coping strategies often exhibit stronger psychological resilience, being more willing to confront issues and seek help from others to solve problems. When facing pressures such as academic challenges, they approach them with a positive mindset, overcoming adversity [ 37 ]. It is believed that adopting positive coping strategies to address problems can enhance college students’ levels of psychological resilience [ 10 , 38 ]. Resilience can significantly and negatively predict depressive symptoms. depressive symptoms, College students with higher levels of resilience tend to define the severity of events less severely when stress events occur, resulting in lower psychological burdens and reduced likelihood of experiencing depressive symptoms [ 10 ]. Additionally, when facing setbacks or stress, individuals who adopt positive coping strategies actively utilize internal and external protective factors to combat current difficulties and pressures, and employ effective emotional control to mitigate the impact, thereby enhancing their levels of psychological resilience and reducing the occurrence of depression.

Chain mediation effect of perceived social support and psychological resilience

This study elucidates that PC/NC perceived social support, and psychological resilience are independent factors influencing depressive symptoms in college students, with perceived social support and psychological resilience playing a mediating role between coping styles and depressive symptoms. The share of total indirect effect values is 73.62% and 40.00%, respectively, with the third chain path accounting for 6.30% and 27.37% of the total effect ratio, respectively. This confirms the existence of this chain mediation effect, although the chain mediation effect is not as pronounced as the individual mediation effects. Positive coping styles not only directly negatively predict depressive symptoms in college students but also exert an indirect influence on depressive symptoms through perceived social support and psychological resilience. Likewise, negative coping styles not only directly positively predict depressive symptoms in college students but also have an indirect impact on depressive symptoms through perceived social support and psychological resilience, thus demonstrating the value and significance of these two mediating variables in reducing the occurrence of depressive symptoms in college students.

Initially, adopting positive coping styles and being able to perceive social support are crucial factors influencing psychological resilience in college students. There exists a relatively stable systemic relationship between students’ social support and psychological resilience, confirming that social support can enhance individuals’ levels of psychological resilience [ 16 ]. Furthermore, coping styles can affect the occurrence of depressive symptoms from both internal and external perspectives. This is because the social support perceived by college students includes not only tangible social support resources but also their subjective perception of social support, with these two factors constituting external and internal protective factors of psychological resilience [ 39 ]. Positive coping and effective adaptation can enhance college students’ perception of social support, enabling them to mobilize personal, familial, and societal protective factors better when facing various life challenges, thereby mitigating or eliminating difficulties and suppressing the onset of depressive symptoms, whereas negative coping styles yield the opposite effect. The chain mediation proposed in this study integrates the research on perceived social support, psychological resilience, and depressive symptoms in college students, facilitating a more comprehensive understanding of the internal mechanisms through which coping styles influence depressive symptoms in college students. This holds significance in advocating for a proactive attitude in college students to confront and resolve difficulties and in increasing attention to the mental health of college students.

Limitations, strengths and future research

The findings of this study hold theoretical value and practical implications, offering a reference basis for improving the mental health of college students. However, there are certain limitations to consider. Firstly, the survey in this study was conducted through self-reporting, which may introduce certain biases. Future research could explore data collection through various methods. Secondly, this study employed a cross-sectional design to investigate the impact of trait coping styles, on depression among college students and its potential mechanisms. However, this research approach does not allow for causal inferences between variables, and further validation of the study’s conclusions could be achieved through longitudinal or experimental research.

In summary, this study aims to improve the mental health of college students by examining how their coping styles, along with their perceived social support and psychological resilience, affect depressive symptoms. The research analyzes the connections between these factors and suggests that positive coping styles may help prevent depression. However, the study has its limitations and future research should use long-term experiments to better understand these relationships. Since depression in college students can be influenced by many factors, future studies should also consider additional variables and use a mix of experimental and longitudinal approaches to more clearly understand how to reduce depression in this group.

Data availability

The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Patient Health Questionnaire-9

Trait Coping Style Questionnaire

Positive coping styles

Negative coping styles

Resilience Scale

Platania GA, Savia Guerrera C, Sarti P, et al. Predictors of functional outcome in patients with major depression and bipolar disorder: a dynamic network approach to identify distinct patterns of interacting symptoms[J]. PLoS ONE. 2023;18(2):e0276822.

Article   PubMed   PubMed Central   Google Scholar  

Guerrera CS, Platania GA, Boccaccio FM, et al. The dynamic interaction between symptoms and pharmacological treatment in patients with major depressive disorder: the role of network intervention analysis[J]. BMC Psychiatry. 2023;23(1):885.

Liu Y, Chen J, Chen K, et al. The associations between academic stress and depression among college students: a moderated chain mediation model of negative affect, sleep quality, and social support[J]. Acta Psychol. 2023;239:104014.

Article   Google Scholar  

Gao L, Xie Y, Jia C, et al. Prevalence of depression among Chinese university students: a systematic review and meta-analysis[J]. Sci Rep. 2020;10(1):15897.

Naja WJ, Kansoun AH, Haddad RS. Prevalence of Depression in Medical students at the Lebanese University and exploring its correlation with Facebook Relevance: a Questionnaire Study[J]. JMIR Res Protocols. 2016;5(2):e96.

Brenneisen Mayer F, Souza Santos I, Silveira PSP, et al. Factors associated to depression and anxiety in medical students: a multicenter study[J]. BMC Med Educ. 2016;16(1):282.

Liu Y, Zhang N, Bao G, et al. Predictors of depressive symptoms in college students: a systematic review and meta-analysis of cohort studies[J]. J Affect Disord. 2019;244:196–208.

Article   PubMed   Google Scholar  

Moutinho ILD, Maddalena N, D C P, Roland RK, et al. Depression, stress and anxiety in medical students: a cross-sectional comparison between students from different semesters[J]. Volume 63. Revista da Associação Médica Brasileira; 2017. pp. 21–8. 1.

Acharya L, Jin L, Collins W. College life is stressful today – emerging stressors and depressive symptoms in college students[J]. J Am Coll Health. 2018;66(7):655–64.

Ibrahim AK, Kelly SJ, Adams CE, et al. A systematic review of studies of depression prevalence in university students[J]. J Psychiatr Res. 2013;47(3):391–400.

Zvauya R, Oyebode F, Day EJ, et al. A comparison of stress levels, coping styles and psychological morbidity between graduate-entry and traditional undergraduate medical students during the first 2 years at a UK medical school[J]. BMC Res Notes. 2017;10(1):93.

Undheim AM, Sund AM. Associations of stressful life events with coping strategies of 12–15-year-old Norwegian adolescents[J]. Eur Child Adolesc Psychiatry. 2017;26(8):993–1003.

Lau Y, Wang Y, Kwong DHK, et al. Are different coping styles Mitigating Perceived stress Associated with depressive symptoms among pregnant women? Are different coping styles Mitigating Perceived stress Associated with depressive symptoms among pregnant women?[J]. Perspect Psychiatr Care. 2016;52(2):102–12.

Ding Y, Yang Y, Yang X, et al. The Mediating Role of coping style in the relationship between Psychological Capital and Burnout among Chinese Nurses[J]. PLoS ONE. 2015;10(4):e0122128.

Gandzha IM. [Immune disorders in internal diseases and the ways for their correction][J]. Vrach Delo, 1978(7): 14–9.

Howard S, Creaven AM, Hughes BM, et al. Perceived social support predicts lower cardiovascular reactivity to stress in older adults[J]. Biol Psychol. 2017;125:70–5.

Barrera M. Distinctions between social support concepts, measures, and models[J]. Am J Community Psychol. 1986;14(4):413–45.

Xin M, Yang C, Zhang L, et al. The impact of perceived life stress and online social support on university students’ mental health during the post-COVID era in Northwestern China: gender-specific analysis[J]. BMC Public Health. 2024;24(1):467.

Sun J, Harris K, Vazire S. Is well-being associated with the quantity and quality of social interactions?[J]. J Personal Soc Psychol. 2020;119(6):1478–96.

Wang J, Chen Y, Chen H, et al. The mediating role of coping strategies between depression and social support and the moderating effect of the parent–child relationship in college students returning to school: during the period of the regular prevention and control of COVID-19[J]. Front Psychol. 2023;14:991033.

Ball S, Bax A. Self-care in Medical Education: effectiveness of health-habits interventions for First-Year Medical Students[J]. Acad Med. 2002;77(9):911–7.

Howe A, Smajdor A, Stöckl A. Towards an understanding of resilience and its relevance to medical training[J]. Med Educ. 2012;46(4):349–56.

Louise Duncan D. What the COVID-19 pandemic tells us about the need to develop resilience in the nursing workforce[J]. Nurs Manag. 2020;27(3):22–7.

Google Scholar  

Luo Y, Wang H. Correlation research on psychological health impact on nursing students against stress, coping way and social support[J]. Nurse Educ Today. 2009;29(1):5–8.

Luo Mming, Hao M, Li Xhuan, et al. Prevalence of depressive tendencies among college students and the influence of attributional styles on depressive tendencies in the post-pandemic era[J]. Front Public Health. 2024;12:1326582.

Zhang Y, Jia Y, MuLaTiHaJi M, et al. A cross-sectional mental-health survey of Chinese postgraduate students majoring in stomatology post COVID-19 restrictions[J]. Front Public Health. 2024;12:1376540.

Blumenthal JA, Burg MM, Barefoot J, et al. Social support, type A behavior, and coronary artery disease.:[J]. Psychosom Med. 1987;49(4):331–40.

Quintiliani L, Sisto A, Vicinanza F, et al. Resilience and psychological impact on Italian university students during COVID-19 pandemic. Distance learning and health[J]. Volume 27. Psychology, Health & Medicine; 2022. pp. 69–80. 1.

Shao R, He P, Ling B, et al. Prevalence of depression and anxiety and correlations between depression, anxiety, family functioning, social support and coping styles among Chinese medical students[J]. BMC Psychol. 2020;8(1):38.

Riedel B, Horen SR, Reynolds A, et al. Mental Health disorders in Nurses during the COVID-19 pandemic: implications and coping Strategies[J]. Front Public Health. 2021;9:707358.

Faisal-Cury A, Savoia MG, Menezes PR. Coping style and depressive symptomatology during pregnancy in a private setting Sample[J]. Span J Psychol. 2012;15(1):295–305.

Platania GA, Varrasi S, Guerrera CS, et al. Impact of stress during COVID-19 pandemic in Italy: a study on dispositional and behavioral dimensions for supporting evidence-based targeted Strategies[J]. Int J Environ Res Public Health. 2024;21(3):330.

Xu Y, Zheng Q, Jiang X, et al. Effects of coping on nurses’ mental health during the COVID-19 pandemic: mediating role of social support and psychological resilience[J]. Nurs Open. 2023;10(7):4619–29.

Kassam S. Understanding experiences of Social Support as Coping resources among immigrant and Refugee women with Postpartum Depression: an Integrative Literature Review[J]. Issues Ment Health Nurs. 2019;40(12):999–1011.

Xu J, Wei Y. Social Support as a moderator of the relationship between anxiety and depression: an empirical study with adult survivors of Wenchuan Earthquake[J]. PLoS ONE. 2013;8(10):e79045.

Luthar SS, Cicchetti D, Becker B. The Construct of Resilience: a critical evaluation and guidelines for future Work[J]. Child Dev. 2000;71(3):543–62.

Lee SH, Cho SJ. Cognitive Behavioral Therapy and Mindfulness-Based Cognitive Therapy for Depressive Disorders[M]//, Kim YK. Major Depressive Disorder: Vol. 1305. Singapore: Springer Singapore, 2021: 295–310.

Thompson G, McBride RB, Hosford CC, et al. Resilience among medical students: the role of coping style and social Support[J]. Teach Learn Med. 2016;28(2):174–82.

Murphy J, McGrane B, White RL, et al. Self-Esteem, meaningful experiences and the Rocky Road—contexts of physical activity that impact Mental Health in Adolescents[J]. Int J Environ Res Public Health. 2022;19(23):15846.

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Acknowledgements

We would like to provide our extreme thanks and appreciation to all students who participated in our study.

This work was financially supported by the National Food Safety Risk Center Joint Research Program [grant number (LH2022GG06)] and the Weifang Medical College Teaching Reform Program (2023YBC008).

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SD and GM conceived and designed the study. HG, WS, WG, and YL undertook the data collection and analysis. SD, QY, YQ, XLand HZ drafted the manuscript. SD and GM reviewed the manuscript. The authors read and approved the final manuscript.

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Dong, S., Ge, H., Su, W. et al. Enhancing psychological well-being in college students: the mediating role of perceived social support and resilience in coping styles. BMC Psychol 12 , 393 (2024). https://doi.org/10.1186/s40359-024-01902-7

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ORIGINAL RESEARCH article

Theileria orientalis ikeda infection does not negatively impact growth performance or breeding soundness exam results in young beef bulls at bull test stations.

• 1 Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
• 2 School of Animal Sciences, Virginia Tech, Blacksburg, VA, United States
• 3 Department of Large Animal Clinical Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
• 4 Department of Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States

Introduction: Theileria orientalis Ikeda genotype is an emerging cattle disease in the US. Since 2017, when T. orientalis Ikeda was discovered in beef cattle in two counties in Virginia, cattle infections have risen to include ~67% of Virginia counties and 14 states. Consistent with New Zealand studies, many infected herds in Virginia were >90% positive upon initial testing without overt evidence of infection. Central bull tests present a unique opportunity to study the effects of T. orientalis Ikeda infections, as bulls from multiple source herds are consolidated. The objective of this study was to determine if infection with T. orientalis Ikeda affected the average daily gain (ADG), adjusted yearling weight (AYW) and breeding soundness of bulls at two test stations in Virginia over a period of years.

Materials and methods: The bulls were fed and housed similarly to compare their growth performance and breeding soundness. For T. orientalis Ikeda testing, DNA was extracted from whole blood for quantitative polymerase chain reaction.

Results: The number of bulls infected with T. orientalis Ikeda at initial delivery to the stations increased significantly over the years studied. Multivariable linear regression models, using Angus bulls from Virginia test stations, indicated no significant effect on ADG or AYW in bulls that became test positive during the test or were positive for the duration, compared to Angus bulls that were negative for the duration. At LOC A, the odds of passing a breeding soundness exam (BSE) were not significantly different for bulls that turned positive during the test or were positive for the duration, compared to bulls that were negative for the duration of the test. At LOC B, bulls that became positive during the test were 2.4 times more likely (95% CI: 1.165–4.995, p = 0.016) to pass their BSE compared to bulls that remained negative throughout the test.

Discussion: We do not suppose that an obscured infection of T. orientalis Ikeda is protective for bulls to pass a BSE. However, this study demonstrates an obscured infection of T. orientalis Ikeda does not negatively affect weight gain or achievement of a satisfactory BSE rating at the central bull test stations in Virginia.

1 Introduction

In 2017, Theileria orientalis genotype Ikeda was first detected in the United States (US) in beef cattle from multiple counties in Virginia ( 1 ). In Virginia between 2019 and 2021, surveillance testing of market cattle for T. orientalis Ikeda, increased from <2 to ~35% of samples testing positive in the Northern and Southwest markets with an overall prevalence of 8.7% in 2021 ( 2 ). This emerging cattle disease has since been discovered in 14 states as of November 2023 ( 3 ). T. orientalis is a tick-borne non-transforming hemoprotozoan ( 4 ) with 11 genotypes as determined by molecular characterizations of the major piroplasm surface protein (MPSP) ( 5 ). Of the 11 genotypes of T. orientalis , type 2 (Ikeda) is meaningfully associated with clinical disease in Australia, New Zealand, Japan ( 4 ) and the US ( 1 ).

The primary biologic vector of T. orientalis Ikeda in New Zealand, Japan ( 4 ) and the US is Haemaphysalis longicornis , the Longhorned tick (LT) ( 6 , 7 ). As of March 2024, the LT has been found in 19 states ( 8 ). The biology of the LT includes parthenogenetic reproduction, aggressive biting, minimal host specificity, and swarming behavior which allows for rapid increases in both population numbers and infested territories ( 9 , 10 ). This biology, and that the LT spends the majority of its lifecycle off the host in the environment, makes LT control measures problematic ( 11 ). The expanding distribution of the LT has followed predictions in the Eastern US ( 12 , 13 ), while expanding farther west, north and south than expected. With the geographic distribution of T. orientalis Ikeda closely mirroring that of the LT, spread of T. orientalis Ikeda to non-endemic regions through expansion of the LT distribution must be anticipated.

Acute clinical infection from T. orientalis Ikeda can cause anemia, icterus, ill-thrift, abortions and death in naïve cattle particularly if they are stressed due to parturition, transportation or a significant change in husbandry ( 1 , 4 , 14 ). A sequela of T. orientalis Ikeda infection is the cattle become asymptomatic carriers, potentially for life ( 15 ). However, in other countries, positive beef herds without overt clinical signs have been described and associated with decreased live weight gain in weaned beef calves ( 16 ) and decreased mean daily gain in suckling beef calves ( 17 ). It was thought the persistently infected cattle could recrudesce into clinical disease particularly under times of stress, however it does not appear to frequently occur ( 18 ). However, theileriosis, even if apparently asymptomatic or in the persistently infected chronic phase, “decreases the fitness of the animal to tolerate other endemic diseases and deficiencies” ( 19 ). Additionally, there are no available treatments or vaccines for T. orientalis in the US, and only one effective treatment available elsewhere requiring extended meat withdrawal and cost ( 4 ). As the LT and cattle theileriosis spread in the US, creating endemicity, it will be crucial to understand the effects of T. orientalis Ikeda infections on beef production parameters such as growth performance, reproductive soundness and longevity within the herd.

The Virginia Beef Cattle Improvement Association has operated bull performance test stations for over 60 years, and currently operates two separate stations annually. Bull test stations provide bull producers an opportunity to compare their bull's growth performance to other bulls in a controlled environment, culminating in a value-added sale based upon the bull's performance. Bull test stations provide an excellent opportunity to study the effects of T. orientalis Ikeda infection on weight gain, as feed and environment are the same for all the bulls at each location. Another advantage of the test stations is that the entry requirements provide a generally homogenous population of bulls with respect to age and health status to study reproductive soundness.

The purpose of this retrospective study was to determine if weight gain or reproductive soundness of bulls at Virginia bull test stations was affected by the bulls' T. orientalis Ikeda status. Our hypothesis was that those bulls that were negative for T. orientalis Ikeda for the duration of the bull test would perform better with respect to gain and reproductive soundness compared to bulls that were positive for the whole duration or became positive during the bull test.

2 Materials and methods

2.1 bull management and growth data.

The bull test stations were operated at two locations in Virginia by the Virginia Beef Cattle Improvement Association. At LOC A (in Louisa County), fall born bulls (SR) were delivered to the test facility in Late June at 8–10 months of age. At LOC B (in Wythe County), both the fall born (SR) and spring-born (JR) bulls were delivered to the test facility in early October at 10–12 and 7–9 months of age respectively. At both stations, growth performance was evaluated over a 112-day period with qualifying bulls sold at a special sale ~6 weeks later. For this study, bulls were placed on the study at the time of delivery to their respective station until BSE's were completed after the growth performance test, which was for ~5 months. All bulls originated from cooperating farms in Virginia or bordering states.

At delivery, bulls were accompanied by a Certificate of Veterinary Inspection, tested negative to anaplasmosis and bovine viral diarrhea (BVD) persistent infection and met post-weaning management and vaccination requirements. Requirements included a 7-strain Clostridial, Pasteurella , and a modified live 5-way respiratory viral vaccination. At delivery, bulls were examined for reproductive soundness by measuring testicular circumference and unsound bulls were eliminated. Bulls received a 7-strain Clostridium booster, 5-way respiratory virus booster, and were treated for internal and external parasites with pour-on Cydectin ® (Elanco Animal Health, Greenfield IN; 5 mg/ml moxidectin, dose 0.5 mg/kg topically) at delivery. Lastly, at delivery, all bulls were bled via the caudal tail vein into an EDTA anticoagulant tube for molecular diagnostics and testing for T. orientalis Ikeda as described below. Only single use needles were used for all injections. The blood sampling was reviewed and approved by the Institutional Animal Care and Use Committee of Virginia Tech (protocol #21-248).

Each bull's attitude and appearance were examined daily, however unless they appeared ill or anorexic, they were only put the through the chute system periodically for weighing. At LOC A, bulls were housed in 12-acre grass lots with the woods on the edges mostly fenced out, in groups of ~50 according to breed, age, and weight. At LOC B, bulls were housed in 12–30-acre grass lots that contained some woods, but grouped similarly. At both locations, a corn-silage based total mixed ration was fed with a target average daily gain (ADG) of 1.6 kg/day, formulated using the National Research Council Nutrient Requirements of Beef Cattle ( 20 ). Following a 14–21-day diet acclimation period, weight gain was measured over a 112-day period. Average daily gain (ADG) and adjusted yearling weight (AYW) were calculated along with ratios for animals of the same breed and age (SR vs. JR) according to Guidelines for Uniform Beef Improvement Programs standards ( 21 ). Because the LOC B test occurred over winter, the bulls were also treated for external parasites in early January. At completion of the test every bull's scrotal circumference was measured, caudal tail vein blood was obtained for T. orientalis Ikeda testing, and SR bulls had a breeding soundness exam (BSE) performed. Approximately 10%−20% of SR bulls did not receive a BSE as they were not sale eligible based on poor test growth performance, structural unsoundness, illness or other issue.

Both bull tests consisted of a 2–3-week transition period, 112 days on feed performance testing, and then an ~6-week period until the sale of the bulls. At the end of the feed performance test, a complete breeding soundness exam (BSE) is performed on sale-eligible bulls. T. orientalis Ikeda was first detected in 2019 when the bulls arrived at the southwest Virginia bull test station, but testing was not conducted on the second location until 2021. An acute case of T. orientalis Ikeda with clinical signs severe enough to require pulling a bull from its group, for exam by bull station staff or a veterinarian, was not noted at either test station to date.

2.2 Breeding soundness exam

Components of the BSE for SR bulls included a brief physical exam, rectal palpation of the accessory sex glands, palpation of the testis, determination of scrotal circumference, and electroejaculation. Electroejaculation utilized the same program for each bull via the Pulsator IV (prior to 2022) and the Pulsator V (after 2022) (Lane Manufacturing, Denver CO). Semen motility was evaluated by wet mount chute side with 30% progressive motility required for a satisfactory rating. Morphology was determined using Eosin-stained slides examined under oil immersion at 100X with 70% normal sperm required for a satisfactory rating. If a bull was deferred at the initial BSE that occurred at the end of the growth performance test, a second BSE was performed ~3 weeks later for final determination of BSE result. For the purposes of this study, a bull was graded either “not-eligible,” satisfactory or unsatisfactory.

2.3 Molecular diagnostics

DNA was extracted from K 2 -EDTA anticoagulated blood (DNeasy blood and tissue kit; Qiagen) following the manufacturer's protocol using pre-warmed (56°C elution buffer. The initial blood volume for DNA extraction was 100 μl. We used a duplex TaqMan-based rtPCR assay to detect the major surface protein 1b (msp1b) gene of Anaplasma marginale and the major piroplasm surface protein (MPSP) gene of T. orientalis as described by our lab previously ( 22 ). The primers and probes for MPSP are specific for T. orientalis but are not genotype-specific. To further characterize genotype a second multiplex rtPCR assay targeting Ikeda, Chitose, and Buffeli genotypes as described by our lab previously ( 22 ).

Each year, the bulls were tested for T. orientalis Ikeda twice; at delivery and at the end of the feed performance test when BSEs were performed on eligible bulls. Based upon the T. orientalis Ikeda results from the start of the test to the end of the feed performance test, bulls were classified as either negative throughout the test (NN), positive throughout the test (PP), or negative on intake and positive at the end of the test (NP). There are two scenarios where a bull could be classified as a NP bull; they were infected shortly before delivery and did not have enough time to become test positive at delivery sampling or they became positive while at the bull test station.

2.4 Statistical analysis

Data were entered into spreadsheets (Excel; Microsoft Corp.) and subsequently analyzed using JMP ® Pro (JMP ® , Version 16. SAS Institute Inc., Cary, NC 1989–2013). Prevalence (95% CI) of bulls positive at intake to the test station were compared for each location by year and tested with a Fisher's exact test at LOC A or Cochran-Armitage test for trend at LOC B. There were three distinct populations of bulls from the two stations; LOC A all SR bulls (2021–2022), LOC B SR bulls (2020–2023) and LOC B JR bulls (2020–2023). Angus was the majority breed in all three populations of bulls with too few bulls of other breeds for analysis, so analyses for average daily gain (ADG) and adjusted yearling weight (AYW) were restricted to purebred Angus bull data due to well documented breed differences expected with gain ( 23 – 25 ). Means for ADG and AYW were determined with 95% confidence intervals (CI). A one-way ANOVA with Dunnets method for means comparison was used to compare the NP and PP bulls to the control group NN. In multivariable linear regression models for ADG and AYW, T. orientalis Ikeda status was forced into each model and other potential covariables included year of test, age and weight at delivery, group within test, and state of origin. The cut-off for potential covariates to be included in the final models was p < 0.05. Then using the Dunnets method, within the model, the T. orientalis Ikeda statuses of NP and PP were compared to the control group NN. To assess effects of infection on eligibility for a BSE, the NP and PP bulls were separately compared to the NN group to determine the ratio of success between the groups, with the 95% CIs and Fisher's exact test. In multivariable logistic regression models for BSE eligibility, T. orientalis Ikeda status was forced into each model and other potential covariables included year of test, ADG ratio, and AYW ratio. The ADG and AYW ratios were used in the BSE models because all breeds were represented in the BSE data, and the ratios normalized the breed differences in gain. To assess effects of infection on achieving a satisfactory rating in the BSE, the NP and PP bulls were separately compared to the NN group to determine the ratio of successes between groups, with the 95% CIs and Fisher's exact test. In multivariable logistic regression models for BSE success, T. orientalis Ikeda status was forced into each model. We used forward stepwise multivariable logistic regression analyses with Bayesian Information Criteria as the stopping rule and p < 0.05 as the cut-off for potential covariates: year, state of origin, pen number, breed, bull conception type, age at delivery, weight at delivery, ADG and AYW.

3.1 Bull test station populations

A total of 584 senior and 416 junior bulls were examined in this study ( Table 1 ). For all three populations the majority of the bulls were purebred Angus, though there was representation from many different breeds. At LOC A, ~92% of the bulls were from Virginia with <8% of bulls from West Virginia and North Carolina. At LOC B for both JR and SR groups, ~62% of the bulls were from Virginia. Approximately 23% of the LOC B bulls were from Tennessee, while <15% of the LOC B bulls were from West Virginia and North Carolina. For the two SR populations of bulls ~68% of the bulls passed a BSE with a satisfactory rating. JR bulls were too young for a BSE. The three populations of purebred Angus bulls used in the growth performance analyses are described in Table 2 .

Table 1 . Descriptive statistics of the three populations of bulls including all breeds; LOC A (years 2021–2022), LOC B seniors (years 2020–2023) and LOC B juniors (years 2020–2023).

Table 2 . Descriptive statistics of the Angus bulls within each subpopulation from the Virginia bull test stations, for the variables used in the multivariable models of average daily gain and adjusted yearling weight.

3.2 Theileria orientalis Ikeda testing

At both Virginia bull test stations, there has been a significant ( p < 0.0001) increase in the percent of bulls that tested positive to T. orientalis Ikeda at delivery over the years tested ( Figure 1 ).

Figure 1 . Theileria orientalis Ikeda percentage of positive bulls on intake to the bull test stations in Virginia for years indicated. LOC A–2021 40.3% (31.8–49.5 95% CI), 2022 64.7% (56.5–72.2 95% CI). LOC B–2019 0.8% (0.1–4.5 95% CI), 2020 10.4% (6.9–15.4 95% CI), 2021 49.1% (42.5–55.7 95% CI), 2022 64.4% (57.7–70.6 95% CI). CI: 95% confidence interval.

3.3 Average daily gain and adjusted yearling weight

With univariate analysis, ADG was significantly higher among NP bulls compared to NN bulls in both the LOC B JR and SR populations and for PP bulls compared to NN bulls among the LOC B SR population ( Table 3 ). After adjusting for year of test, age at delivery and weight at delivery; there were no significant differences regardless of T. orientalis Ikeda status.

Table 3 . Unadjusted and adjusted ADG (kg/day) of the NP and PP bulls, compared to NN bulls, within the three populations at the Virginia bull test stations for the years indicated.

In the univariate comparison, LOC B senior bulls with a NP T. orientalis Ikeda status had a significantly higher AYW compared to NN bulls ( Table 4 ). However, after adjustment for year of test, age at delivery and weight at delivery, this difference vanished. There were no significant differences in AYW for any comparisons between NN and NP senior bulls in either the LOC B or LOC A ( Table 4 ).

Table 4 . Unadjusted and adjusted AYW (kg) of the NP and PP bulls, compared to the NN bulls, within the three populations at the Virginia bull test stations for the years indicated.

3.4 Breeding soundness exam

There was no significant difference in the likelihood of a NN bull being eligible for a BSE as compared to the NP or PP bulls, at either location ( p > 0.3 for all multivariable models). BSE eligibility was determined in part by gain during the feed test period, therefore it is no surprise that at both locations the ADG ratio ( p < 0.002) and AYW ratio ( p < 0.002) were significantly lower if the bull was ineligible for a BSE, regardless of T. orientalis Ikeda status or year of test.

Among bulls of different T. orientalis Ikeda status, the only significant difference was a higher likelihood for NP bulls to achieve a satisfactory BSE, compared to NN bulls at LOC B ( Table 5 ). None of the tested covariables were found to be significant at LOC A, therefore only univariate results were reported. However, at LOC B year of test was significant ( p = 0.0005) when comparing NN and NP bulls regardless of T. orientalis Ikeda status. And when comparing NN and PP bulls at LOC B, ADG ratio increased significantly ( p < 0.01) with the odds of achieving a satisfactory rating on the BSE regardless of T. orientalis Ikeda status.

Table 5 . The odds of Theileria orientalis Ikeda status NP or PP bull, using the NN bulls as reference, achieving a satisfactory rating in the BSE at the Virginia bull test stations for the years indicated.

4 Discussion

Cattle T. orientalis Ikeda infections are spreading and already endemic in some areas where the LT is present in the US. At this time, a true prevalence of Ikeda infection in Virginia cattle is difficult to ascertain as the majority of surveillance sampling has been convenience sampling at Virginia livestock markets ( 2 ) or sampling of specific populations of cattle, such as this study. The increasing number of bulls delivered to the test stations infected over the years studied and the number of bulls obtaining a NP status during our study, confirm T. orientalis Ikeda is highly infective. Other authors have found that virulence is highly variable ( 19 ) due to the presence of multifactorial risk factors, the relationship of which have yet to be elucidated. This study focused on whether infection in young beef bulls negatively impacted bull performance. These overarching effects of infection are pertinent for beef cow/calf producers, as the vast majority use live cover for breeding. We found no significant differences in test ADG or AYW between Angus bulls that were negative for T. orientalis Ikeda infection at both the start and end of the test period; those that were positive at the start and end; or those that became infected during the 5–6 months on test. The bulls at the Virginia bull test stations were all beef breed bulls, 7–14 months of age, and they all gained appropriately for the nutrition provided and according to previous literature ( 25 ). Similarly, previous work in 2-year-old dairy bulls that were experimentally infected with T. orientalis Ikeda, through intravenous injection of infected blood, found no differences in gain, live weight, hematocrit, or temperature between the infected ( n = 10) and non-infected ( n = 7) bulls over the 20-week study ( 26 ). At day 70 post-infection, a subset ( n = 4) of the infected bulls started gaining significantly less than the other six bulls of the infected group. However, a reason for this apparent gain difference among infected animals was not identified.

Two other studies possibly indicate younger beef calves may experience reduced gain if infected with T. orientalis Ikeda. In Australia, a group of 30 weaned beef calves negative for T. orientalis were moved to an area where T. orientalis Ikeda was proven to be in both H. longicornis ticks and resident cattle ( 16 ). By 3 weeks after arrival, all 30 calves were found to be infected with T. orientalis Ikeda. The authors calculated average daily live weight gain (ADLG) every 3 weeks and there were two periods of significantly lower gain in the 24 weeks, the second of which did not appear to be related to parasitemia and was considered a period of declining nutrition as it occurred in the middle of winter ( 16 ). At 24 weeks the weight of the 30 calves was less than was expected based upon regional benchmarks of ADLG from years prior. The lack of uninfected calves for comparison and the recognized period of poor nutrition makes these findings difficult to interpret. A New Zealand study investigated gain in 123 suckling beef calves on pasture with their dams that were ~2 months old at the start of the study and 5–7 months old at the end ( 17 ). The bred dams had been purchased from an area in New Zealand that was endemically stable for T. orientalis Ikeda but were not tested prior to the study and were then moved to a new area of sparser tick distribution prior to calving. Calves that became infected during the study, had a lower mean daily gain in the latter half of the study yielding a ~4.5 kg lower live weight at the end of the study. The T. orientalis infection status, genetics and milk production of their dams was unknown. For some chronic infectious diseases, negative health and production effects occur during times of stress or less than optimal nutrition ( 27 ). Bulls at the Virginia test stations are managed on a high plane of nutrition with an extensive preventive program against other diseases. This may reduce the stresses upon their immune system which would equip the bulls to better manage T. orientalis Ikeda parasitemia.

Other studies have used experimental inoculation of infected blood or pasture-based tick-borne transmission with evidence of T. orientalis Ikeda infected ticks proven prior to putting cattle in the environment. At both locations of the Virginia bull test stations, no ticks were found on the bulls at delivery or at any processing or weighing event during the test. Additionally, all bulls were treated with an ectoparasiticide upon delivery (and a second time at LOC B), and individual needles are used for all injections throughout the test. A bull that was infected shortly before delivery may have not reached a detectable parasitemia at delivery. And to limit stress and maintain a viable test station, the bulls were not examined frequently for transient fevers or anemia, therefore a mild but acute infection could have been missed. A low environmental burden of ticks creating a minimal number of ticks on the bulls could also have been overlooked with infrequent examinations. A low number of ticks on the bulls or mechanical transmission by hematogenous flies or lice may be responsible for the transmission of the T. orientalis Ikeda. Cattle infected by mechanical transfer or smaller tick burdens take longer to turn positive, have a lower parasitemia, experience a mild anemia if any, are unlikely to experience severe symptoms of disease, and could go undetected unless tested regularly ( 6 , 26 , 28 ). This also may have mitigated the negative effects of infection that occurred at the test stations.

With regard to reproductive soundness, there was no significant difference in the odds of the NP or PP bulls achieving a satisfactory rating compared to the NN bulls at LOC A. This is in agreement with a New Zealand study ( 29 ) where dairy bulls were serially tested for reproductive soundness following infection and no changes were found in semen quality. At our LOC B bull test, we found that NP bulls were significantly more likely to achieve a satisfactory rating compared to NN bulls. We do not suggest that T. orientalis Ikeda infection enhances the reproductive soundness of the NP bulls. However, it is evidence that acquiring a T. orientalis Ikeda infection, without having clinical diseases, does not appear to hinder the future reproductive soundness of younger bulls.

This New Zealand study ( 29 ), also included serial measurements of libido and found a 2-week period of decreased libido following infection. This was thought to be a manifestation of the mild anemia and anorexia. In the US, the bull BSE does not typically include a measurement of libido ( 30 ). In our study the BSE was not serially performed but was only done at the end of the feeding period to ensure reproductive soundness before sale and libido was not assessed. Therefore, we could not assess transient effects on bull reproductive performance.

In a related study, bulls that became clinically ill following infection with A. marginale , another cause of clinical bovine infectious anemia, had significantly decreased scrotal circumference and reduced sperm quality for weeks beyond clinical resolution of the disease ( 31 ). There is anecdotal evidence from regional herds in Virginia, showing a gap of 5–7 weeks in pregnancies when bulls were acutely infected in the field during the breeding season (Lahmers, K.K. personal communication). Similarly, the Northland Index herd in New Zealand, reported a 47% decrease in successful pregnancies during their 6-week natural breeding season following infection with theileriosis ( 19 ). A clinical infection of T. orientalis Ikeda during the breeding season, resulting in decreased libido or decreased sperm quality, would explain these results. Acute disease is more likely with a heavy infected tick load because the LT amplifies infective sporonts by the thousands in their salivary glands, which are then deposited into the bovine host when feeding ( 32 , 33 ).

There is large variability in the herd-level manifestation of clinical theileriosis in both New Zealand and US outbreaks with some herds experiencing up to a 5% mortality rate and others becoming nearly 100% T. orientalis Ikeda positive without any signs of clinical disease ( 1 , 3 , 19 ). Whether this variability is due to nutritional status, genetics or method of transmission is unknown at this time ( 34 ). However, the results of this study are encouraging in that bulls that are positive for T. orientalis Ikeda or acquire the infection without overt clinical signs, continue to grow appropriately and are able to achieve a satisfactory rating on a BSE exam.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The animal studies were approved by Virginia Tech Institutional Animal Care and Use Committee. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent was obtained from the owners for the participation of their animals in this study.

Author contributions

SGu: Conceptualization, Data curation, Formal analysis, Writing – original draft, Writing – review & editing, Investigation. SGr: Methodology, Writing – review & editing, Data curation. JC: Investigation, Writing – review & editing, Methodology. ST: Methodology, Writing – review & editing. AA: Methodology, Writing – review & editing. LH: Methodology, Writing – review & editing, Formal analysis. KL: Funding acquisition, Methodology, Resources, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Development of Control Strategies for Theileria orientalis, USDA ARS Nonassistance cooperative agreement (8/1/22-7/30/25), Surveillance for Theileria orientalis, USDA APHIS Cooperative agreement (8/1/21-7/30/23; AP21VSSP0000C068) to KL.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Oakes VJ, Yabsley MJ, Schwartz D, LeRoith T, Bissett C, Broaddus C, et al. Theileria orientalis Ikeda genotype in cattle, Virginia, USA. Emerg Infect Dis. (2019) 25:1653–9. doi: 10.3201/eid2509.190088

PubMed Abstract | Crossref Full Text | Google Scholar

2. Telionis A, Lahmers K, Todd M, Carbonello A, Broaddus CC, Bissett CJ, et al. Distribution of Theileria orientalis in Virginia Market Cattle, 2018–2020. Pathogens. (2022) 11:1353. doi: 10.3390/pathogens11111353

3. Currin JF, Lahmers K, Eastwood G, Day ER, Dellinger TA, McCormick LE. Asian longhorned tick and Theileria orientalis Ikeda: Current Thoughts and Understandings . (2023). Available online at: https://www.pubs.ext.vt.edu/content/pubs_ext_vt_edu/en/APSC/apsc-196/apsc-196.html . (accessed February 5, 2024).

Google Scholar

4. Watts JG, Playford MC, Hickey KL. Theileria orientalis: a review. NZ Vet J. (2016) 64:3–9. doi: 10.1080/00480169.2015.1064792

5. Sivakumar T, Hayashida K, Sugimoto C, Yokoyama N. Evolution and genetic diversity of Theileria . Infect Genet E . (2014) 27:250–63. doi: 10.1016/j.meegid.2014.07.013

6. Dinkel KD, Herndon DR, Noh SM, Lahmers KK, Todd SM, Ueti MW, et al. A U.S. isolate of Theileria orientalis , Ikeda genotype, is transmitted to cattle by the invasive Asian longhorned tick, Haemaphysalis longicornis. Parasit Vectors . (2021) 14:157. doi: 10.1186/s13071-021-04659-9

7. Thompson AT, White S, Shaw D, Egizi A, Lahmers K, Ruder MG, et al. Theileria orientalis Ikeda in host-seeking Haemaphysalis longicornis in Virginia, USA. Ticks Tick-Borne Dis. (2020) 11:101450. doi: 10.1016/j.ttbdis.2020.101450

8. USDA APHIS. National Haemaphysalis longicornis (Asian longhorned tick) Situation Report. (2024). Available online at: https://www.aphis.usda.gov/sites/default/files/longhorned-tick-sitrep-2024-03-07.pdf . (accessed March 8, 2024).

9. Heath A. Biology, ecology and distribution of the tick, Haemaphysalis longicornis Neumann (Acari: Ixodidae) in New Zealand. NZ Vet J. (2016) 64:10–20. doi: 10.1080/00480169.2015.1035769

10. Trout Fryxell RT, Vann DN, Butler RA, Paulsen DJ, Chandler JG, Willis MP, et al. Rapid discovery and detection of Haemaphysalis longicornis through the use of passive surveillance and collaboration: building a state tick-surveillance network. Int J Environ Res Public Health. (2021) 18:7980. doi: 10.3390/ijerph18157980

11. Schappach BL, Krell RK, Hornbostel VL, Connally NP, Gouge D. Exotic Haemaphysalis longicornis (Acari: Ixodidae) in the United States: biology, ecology, and strategies for management. J Integr Pest Manag. (2020) 11:1–11. doi: 10.1093/jipm/pmaa019

Crossref Full Text | Google Scholar

12. Raghavan RK, Barker SC, Cobos ME, Barker D, Teo EJM, Foley DH, et al. Potential spatial distribution of the newly introduced long-horned tick, Haemaphysalis longicornis in North America. Sci Rep. (2019) 9:498. doi: 10.1038/s41598-018-37205-2

13. Rochlin I. Modeling the asian longhorned tick (Acari: Ixodidae) suitable habitat in North America. J Med Entomol. (2019) 56:384–91. doi: 10.1093/jme/tjy210

14. Forshaw D, Alex SM, Palmer DG, Cotter J, Roberts WD, Jenkins C, et al. Theileria orientalis Ikeda genotype infection associated with anaemia, abortion and death in beef cattle in Western Australia. Aust Vet J. (2020) 98:290–7. doi: 10.1111/avj.12937

15. Yam J, Bogema D, Jenkins C. Oriental theilieriosis. In:Abubakar M, Perera PF, , editors. Ticks and Tick-Borne Pathogens . Rijeka: IntechOpen (2019). p. 11. doi: 10.5772/intechopen.81198

16. Emery D, Zhang S, Loo C, Shirley C. A longitudinal study of infection with genotypes of Theileria orientalis in calves and introduced cattle at Dorrigo, New South Wales, and the effect on weight gains. Vet Parasitol. (2021) 296:109487. doi: 10.1016/j.vetpar.2021.109487

17. Lawrence KE, Lawrence BL, Hickson RE, Hewitt CA, Gedye KR, Fermin LM, et al. Associations between Theileria orientalis Ikeda type infection and the growth rates and haematocrit of suckled beef calves in the North Island of New Zealand. NZ Vet J. (2019) 67:66–73. doi: 10.1080/00480169.2018.1547227

18. Lawrence KE, Gedye K, Pomroy WE. A longitudinal study of the effect of Theileria orientalis Ikeda type infection on three New Zealand dairy farms naturally infected at pasture. Vet Parasitol. (2019) 276:108977. doi: 10.1016/j.vetpar.2019.108977

19. Lawrence K, Gedye K, McFadden A, Pulford D, Heath A, Pomroy W, et al. Review of the New Zealand Theileria orientalis Ikeda type epidemic and epidemiological research since 2012. Pathogens. (2021) 10:1346. doi: 10.3390/pathogens10101346

20. National National Academies of Sciences Engineering and Medicine. Nutrient Requirements of Beef Cattle: Eighth Revised Edition . Washington, DC: The National Academies Press (2016).

PubMed Abstract | Google Scholar

21. BIF Guidelines Wiki. Guidelines for Uniform Beef Improvement Plans . (2023). Available online at: http://guidelines.beefimprovement.org/index.php?title=Guidelines_for_Uniform_Beef_Improvement_Programsandoldid=2679 (accessed February 5, 2024).

22. Oakes VJ, Todd SM, Carbonello AA, Michalak P, Lahmers KK. Coinfection of cattle in Virginia with Theileria orientalis Ikeda genotype and Anaplasma marginale . J Vet Diagn Invest . (2022) 34:36–41. doi: 10.1177/10406387211057627

23. Cain M, Wilson L. Factors influencing individual bull performance in central test stations. J Anim Sci. (1983) 57:1059–66. doi: 10.2527/jas1983.5751059x

24. Tong A. Effects of initial age and weight on test daily gains of station-tested bulls. Can J Anim Sci. (1982) 62:671–8. doi: 10.4141/cjas82-082

25. Townsend HGG, Meek AH, Lesnick TG, Janzen ED. Factors associated with average daily gain, fever and lameness in beef bulls at the saskatchewan central feed test station. Ca J Vet Res. (1989) 53:349–54.

26. Lawrence KE, Gibson M, Hickson RE, Gedye K, Hoogenboom A, Fermin L, et al. Experimental infection of Friesian bulls with Theileria orientalis (Ikeda) and effects on the haematocrit, live weight, rectal temperature and activity. Vet Parasitol Reg Stud Rep. (2018) 14:85–93. doi: 10.1016/j.vprsr.2018.09.004

27. Carroll JA, Forsberg NE. Influence of stress and nutrition on cattle immunity. Vet Clin North Am Food Anim Pract. (2007) 23:105–49. doi: 10.1016/j.cvfa.2007.01.003

28. Hammer JF, Jenkins C, Bogema D, Emery D. Mechanical transfer of Theileria orientalis : possible roles of biting arthropods, colostrum and husbandry practices in disease transmission. Parasit Vectors. (2016) 9:34. doi: 10.1186/s13071-016-1323-x

29. Gibson MJ, Lawrence KE, Hickson RE, How R, Gedye KR, Jones G, et al. Effects of Theileria orientalis Ikeda type infection on libido and semen quality of bulls. Anim Reprod Sci. (2020) 214:106312. doi: 10.1016/j.anireprosci.2020.106312

30. Koziol JH, Armstrong CL. Society for Theriogenology Manual for Breeding Soundess Examination of Bulls . Providence, RI: Society for Theriogenology (2018).

31. Lovett AC, Reppert EJ, Jaeger JR, Kang Q, Flowers MR, Bickmeier NP, et al. Satisfactory breeding potential is transiently eliminated in beef bulls with clinical anaplasmosis. BMC Vet Res. (2022) 18:381. doi: 10.1186/s12917-022-03470-7

32. Florin-Christensen M, Schnittger L. Piroplasmids and ticks: a long-lasting intimate relationship. FBL. (2009) 14:3064–73. doi: 10.2741/3435

33. Shaw M. Theileria development and host cell invasion. In:DAE Dobbelaere, DJ McKeever, , editors. World Class Parasites: Volume 3 Theileria . New York, NY: Kluwere Academic Publishers (2002), p. 1–23. doi: 10.1007/978-1-4615-0903-5_1

34. Hickson RE, Lawrence BL, Lawrence KE, Gedye K, Fermin LM, Coleman LW, et al. Genetic susceptibility to Theileria orientalis (Ikeda) in Angus- and Hereford-sired yearling cattle born to dairy cattle on an endemically infected farm in New Zealand. New Zeal J Agric Res. (2023) 1–9. doi: 10.1080/00288233.2023.2181360

Keywords: beef cattle, Theileria orientalis (Ikeda), average daily gain (ADG), breeding soundness evaluation (BSE), bull

Citation: Guynn SR, Greiner SP, Currin JF, Todd SM, Assenga A, Hungerford LL and Lahmers KK (2024) Theileria orientalis Ikeda infection does not negatively impact growth performance or breeding soundness exam results in young beef bulls at bull test stations. Front. Vet. Sci. 11:1432228. doi: 10.3389/fvets.2024.1432228

Received: 13 May 2024; Accepted: 03 July 2024; Published: 18 July 2024.

Reviewed by:

Copyright © 2024 Guynn, Greiner, Currin, Todd, Assenga, Hungerford and Lahmers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sierra R. Guynn, sguynn@vt.edu

† These authors share last authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Reporting on ai mizzou has the experts you need..

July 16, 2024

July 15, 2024 Contact: Courtney Perrett,  [email protected]

Artificial intelligence (AI) stands as the frontier of technological revolution — poised to reshape every facet of our lives. The University of Missouri, a leader in AI research, features AI experts in disciplines such as law, engineering, education, the arts and health sciences.

If you are reporting on AI, we would be happy to connect you with the right expert. Below you will find a sampling of them. Once you select the best expert for your story,  contact us  to arrange an interview.

MU experts in artificial intelligence

• Kevin Brown (digital media and theater) As a professor of digital media and performance studies in the College of Arts and Science, Kevin Brown researches the ways in which digital media intersects with performance, including the role of AI, robotics, video games and other factors in the construction of identity, gender, class and community.
• Prasad Calyam (engineering) Prasad Calyam, the Greg L. Gilliom Professor of Cyber Security in Mizzou’s College of Engineering, studies the role of AI across many different disciplines. He and collaborators have worked to integrate automation and AI to help news organizations sort through story pitches. He’s currently helping determine how much we can trust AI-powered autonomous vehicles. And recently, he studied whether popular chatbots can pass a cybersecurity certification test to help protect digital data. 
• Jianlin (Jack) Cheng (engineering) A Curators’ Distinguished Professor of electrical engineering and computer science in Mizzou’s College of Engineering, Jack Cheng is known for his groundbreaking work around AI-based protein structure prediction. His AI methods were ranked among top predictors in the last nine rounds of the biennial worldwide competition on protein structure prediction between 2006 and 2022. The research has exerted fundamental impacts on both basic and applied biomedical sciences, such as the design of drugs and vaccines.
• J. Scott Christianson (business) J. Scott Christianson, a teaching professor in Mizzou’s Robert J. Trulaske, Sr. College of Business, focuses on AI’s impact on society and geopolitics. He has been featured in numerous articles, mainly on AI, deepfakes and misinformation. Christianson’s insights provide a valuable perspective on AI technologies’ practical applications and implications.
• Dennis Crouch (law) Director of the Center for Intellectual Property and Entrepreneurship at Mizzou’s School of Law, Dennis Crouch is no stranger to new technology. His research has focused on the influence of AI as an inventing tool in patent law. Crouch has published extensively on the relationship between AI and intellectual property, privacy and speech. He also advises generally on the role of AI in the profession of law and in law education.
• Noah Glaser (education) An exemplar of the Missouri Method — an innovative, hands-on approach to education — and professor of information science and learning technologies in the College of Education and Human Development, Noah Glaser conducts research focusing on how AI and virtual reality headsets can simulate training opportunities for autistic people and those with other neurological and developmental disorders.
• Jared Schroeder (journalism) While much AI expertise is technical, Jared Schroeder, a professor in the Missouri School of Journalism, is an expert on the implications of AI as it applies to open discourse in society, democracy and elections, misinformation and disinformation and regulation. In his new book, “The Structure of Ideas: Mapping a New Theory of Free Expression in the AI Era,” Schroeder addresses how AI shapes the marketplace of ideas.
• Sharan Srinivas (engineering) Sharan Srinivas, a professor of industrial and systems engineering, specializes in integrating AI with operations engineering to enable smarter, faster, and more informed decision-making across various domains. His expertise spans health care operations, transportation and logistics, smart service systems and supply chain management. Recently, he used AI to sort through thousands of customer reviews to identify where airlines are falling short and leveraged AI to develop a tool that can predict the trend in steel prices.
• Dong Xu (engineering) As a leading expert on AI in Mizzou’s College of Engineering, Dong Xu has been immersed in the realm of machine learning for more than two decades. Xu is a professor of electrical engineering and computer science whose research examines the interface between bioinformatics and deep learning. Co-founder of the International Journal of AI and Robotics, Xu’s research runs the AI gamut, and he’s comfortable discussing the past and future of this technology.
• Jianfeng Zhou (agriculture systems) Jianfeng Zhou is a professor of agricultural systems technology in the College of Agriculture, Food and Natural Resources. Zhou’s research at the intersection of precision and digital agriculture focuses on AI-enabled plant phenotyping, precision livestock farming and robotic technologies for crop harvesting. Zhou offers insights into how AI is revolutionizing the future of farming. Among his latest ventures: refining the use of the electric, autonomous Monarch tractor that recently came to Mizzou.

This story was originally published by Show Me Mizzou .

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How Behavioral Factors Shape Retirement Wealth

July 15, 2024 • 10 min read.

A recent Wharton conference explored how noncognitive skills, mortality beliefs, aging, and gender disparities impact retirement wealth.

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Wharton’s Olivia S. Mitchell states the stark truth about saving: “Let’s be honest, saving is no fun. People don’t get ‘utils’ (or utility) out of saving. They get utils out of spending. Therefore we must devise new ways to make saving more enjoyable.”

Retirees can get those utils if their savings, including pensions or annuities, are sufficient to finance their lifestyle and health needs. Much depends, of course, on how wisely they saved and invested pre-retirement, as well as their spending patterns in retirement. Driving all that are several behavioral factors, which a recent Wharton conference explored.

About 30 experts delved into these and more topics at the 2024 Pension Research Council symposium , titled “Retirement Saving, Investment, and Spending: New Lessons from Behavioral Research.” Mitchell, a professor of business economics and public policy as well as executive director of the Council, joined Wharton finance professor Nikolai Roussanov to host the two-day event, which marked the Council’s 70th anniversary.

The symposium explored key behavioral factors impacting retirement wealth creation, including conscientiousness and emotional stability, how long people expect to live, how much they expect to spend in retirement, and the decline of cognitive skills with advancing age. Other factors include gender differences placing women at a disadvantage in their wherewithal to save, and shortcomings in retirement plan design.

Knowledge of retirement saving opportunities, along with financial literacy and numeracy, are, of course, prerequisites for retirement preparedness, but they are not enough. Noncognitive skills — soft personality traits like conscientiousness, stress resistance, grit, and locus of control (people’s beliefs about the extent to which they can control events in their lives) — also impact retirement readiness, according to Kim Peijnenburg, a professor at the EDHEC Business School in Nice, France.

Conscientiousness reflects the tendency to be organized, practical, persistent, self-disciplined, and achievement-oriented. It is the most important of the “Big Five” personality traits affecting financial decisions, Peijnenburg noted. The other four are openness to experience, extroversion, agreeableness, and neuroticism.

Peijnenburg and EDHEC colleague Gianpaolo Parise reported that those with high conscientiousness plan systematically for retirement, and they also have more financial wealth than those at the other end of the spectrum. Their study included 13,145 individuals in the Netherlands, between 2008 and 2017.

The top finding was that people in the lowest quintile of emotional stability had a 10% higher probability of being in financial distress, such as being irregular with mortgage payments or rent or utility bills. By contrast, those in the highest quintile of conscientiousness and emotional stability had only a 1% probability of being in financial distress.

Mortality Beliefs and Suboptimal Savings

People’s perceptions of their own longevity also have implications for their retirement planning. According to Arizona State University finance professor Rawley Z. Heimer, many underestimate how long they will live when they are young, which leads them to consume and spend recklessly, save less, and make suboptimal decisions for retirement planning. But as they get older, they overestimate their lifespans, rein in spending, and go slow on drawing down their retirement assets. Somewhere between those two states, they realize they haven’t saved enough for retirement and start to invest more in stocks.

People who lean toward subjective beliefs tend to have incorrect forecasts about several economic variables, such as housing prices, stock market returns, and their own employment. Those who rely on actuarial probabilities — an objective approach — might save more for retirement, he noted.

The Role of “Others” in Retirement Saving

Neighbors, peers, family members, coworkers, and financial advisors also shape how much people save for retirement. Michael Haliossos, chair of microeconomics and finance at Goethe University in Frankfurt, shared his research findings exploring how “others” help boost wealth accumulation.

One of his projects studied neighbors’ peer effects, using data from a Swedish refugee allocation program between 1987 and 1991. The authors tracked participants for the next 10-20 years to identify outcomes in private retirement accounts and stockholding. The top finding was that having more neighbors with college-level economics or business education promoted immigrants’ retirement saving. Another finding was that refugees with a high school certificate and who had neighbors with degrees in business and economics were more likely to be participating in private retirement accounts and hold stock over the next 10 to 15 years.

A second project studied the effects of greater local wealth inequality on people’s later wealth mobility. In such a setting, college-educated households took on more asset risks later in life, such as investing in stocks, housing, and self-employment. Thus, they achieved greater wealth, whereas others who were not similarly motivated were left behind. The implication of those findings is that policymakers need to find ways to empower less-educated households in their financial behavior, Haliossos said.

A different project compared advice on the share of risky assets in retirement saving portfolios given by professional financial advisors with advice provided by lay advisors, such as family and peers. He found that professional advisors tended to recommend a lower allocation to risky assets for retirement than lay advisors. Also, professional advisors were influenced by their own income, age, risk aversion, and risk exposure in their own portfolios, when they offered clients advice. The final study looked at the effects on financial behavior of stress caused by events like the COVID crisis, a financial crisis, or a war. This study showed that such cognitive loads preoccupy one’s mind, draining people’s ability to concentrate and perform important tasks.

“Let’s be honest, saving is no fun. People don’t get ‘utils’ (or utility) out of saving. They get utils out of spending.” — Olivia S. Mitchell

The Real Drivers of the Gender Gap in Wealth Creation

Behavioral factors also influence opportunities women get to save for retirement, compared to those for men. Motherhood and caregiving burdens mean that women have fewer years than men in the paid workforce, lower income trajectories, and a higher incidence of part-time work, said Vickie Bajtelsmit, professor emerita of finance at Colorado State University, who has researched the gender gap in retirement saving.

Consequently, women are penalized both in their employer retirement plans and in Social Security. Segregation also reduces their opportunities to earn and save: The top occupations for women remain teaching, nursing, retailing, and social work, Bajtelsmit noted.

Although conventional wisdom holds that women are more conservative investors than men, Bajtelsmit said that this is an incomplete story. Instead, their lower retirement wealth is mainly due to gender differences in labor markets and household responsibilities. For this reason, solutions to bridge the gender wealth gap must also focus on the labor market and lifelong financial literacy education. Solutions could include continued pressure on the gender pay gap, providing women with broader retirement plan access, and prorated benefits for part-time work, she said.

How Aging Affects Financial and Health Decisions

Older Americans face yet another set of challenges, including mild to severe cognitive impairments that affect their abilities to make financial and health decisions. These impact Social Security claiming choices and end-of-life transfers of wealth. At the same time, many people lose important resources such as social networks and community interactions as they age. Aging is also associated with losing loved ones, including their spouses, friends, and people of the same age.

Such cognitive, contextual, and psychosocial variables are key determinants of decision-making ability, according to Patricia Boyle, professor of behavioral sciences and neuropsychology at the Rush Alzheimer’s Disease Center in Chicago, and Gary Mottola, research director for the FINRA Investor Education Foundation. They shared findings from the Rush Memory and Aging Project, which has tracked about 4,000 older adults from the Chicago area annually since 1987.

Difficulties in making financial choices and understanding the factors at play seem to be an indicator of adverse health outcomes to come, Boyle noted. Someone who has trouble with difficult choices is more likely to develop dementia, for example, or go on to develop more cognitive problems over time, she said.

One finding of the Rush project is that lower levels of cognition are related to lower levels of financial and health decision-making. Another finding was that low levels of financial and health literacy were related to low levels of decision-making. The rate of decline in those levels was related to scam susceptibility and psychological well-being.

In one experiment, the authors gauged how participants responded to a scam offer. The participants received emails, direct mail, and phone calls from a fraudulent marketing campaign conducted by a fictitious agency called the U.S. Retirement Protection Task Force. They were told that their retirement savings accounts had been hacked, and that the task force wanted the last four digits of their Social Security numbers.

Most of the roughly 650 participants (69%) did not engage with the campaign. Some were skeptical (15%); they spoke to the fraudster, asked questions, and sometimes berated him, Mottola said. Nevertheless, 16% of the participants fully engaged with the campaign, providing the information requested without skepticism, although not all revealed their Social Security numbers. None of those calls were recorded, nor did the fraudsters use high-pressure tactics.

The key takeaway here was that 16% number was too high, Mottola said. The full engagement group had the lowest levels of financial literacy and lowest levels of scam awareness. The skeptical engagement group had the highest cognition, which he noted was surprising.

How “Undersum Bias” Hurts Retirement Saving

Many people also underestimate how much money they need to retire comfortably, or the probability of unanticipated expenses, said Shane Timmons, a senior research officer at the Economic and Social Research Institute in Dublin. He documented an “undersum bias” (a term coined by University of Buffalo marketing professor Indranil Goswami) that led people to be less financially prepared for retirement.

Timmons shared highlights from work that he and University of Galway lecturer Féidhlim McGowan conducted on undersum bias and the various ways in which it plays out. He noted that undersum bias occurs when people do not fully comprehend concepts like compound interest, and as a result, underestimate how money can accumulate. They also underestimate the probability of unexpected financial shocks and, therefore, put aside less money than what they need for emergency expenses. Additionally, they underestimate the accumulated impact of several financial shocks, which implies that this bias is an impediment to precautionary saving.

Interventions can help, Timmons and Galway found. They used a “nudge and boost” communication tactic to encourage participants to open savings accounts. That led to a larger-than-expected increase in saving account uptake (over 25%); it was especially helpful for the lower-income sample. They also showed that simple infographics explaining the cumulative probability of financial shocks can also motivate a saving habit.

All said, pushing people to save more may not always be the best approach. Accordingly, Mitchell speculated that it could be unwise in some cases to incentivize the poor to save more. That is, some low-wage workers might do better to pay bills and feed their children, in view of the 90% replacement of pre-retirement income that Social Security will pay, she noted.

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