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• Content Analysis | Guide, Methods & Examples

Content Analysis | Guide, Methods & Examples

Published on July 18, 2019 by Amy Luo . Revised on June 22, 2023.

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

• Books, newspapers and magazines
• Speeches and interviews
• Web content and social media posts
• Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding).  In both types, you categorize or “code” words, themes, and concepts within the texts and then analyze the results.

Table of contents

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis, other interesting articles.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyze.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects or concepts in a set of historical or contemporary texts.

Quantitative content analysis example

To research the importance of employment issues in political campaigns, you could analyze campaign speeches for the frequency of terms such as unemployment , jobs , and work  and use statistical analysis to find differences over time or between candidates.

In addition, content analysis can be used to make qualitative inferences by analyzing the meaning and semantic relationship of words and concepts.

Qualitative content analysis example

To gain a more qualitative understanding of employment issues in political campaigns, you could locate the word unemployment in speeches, identify what other words or phrases appear next to it (such as economy,   inequality or  laziness ), and analyze the meanings of these relationships to better understand the intentions and targets of different campaigns.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

• Finding correlations and patterns in how concepts are communicated
• Understanding the intentions of an individual, group or institution
• Identifying propaganda and bias in communication
• Revealing differences in communication in different contexts
• Analyzing the consequences of communication content, such as the flow of information or audience responses

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• Unobtrusive data collection

You can analyze communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

• Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

• Highly flexible

You can conduct content analysis at any time, in any location, and at low cost – all you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions, leading to various types of research bias and cognitive bias .

• Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct  research question .

Example research question for content analysis

Is there a difference in how the US media represents younger politicians compared to older ones in terms of trustworthiness?

Next, you follow these five steps.

1. Select the content you will analyze

Based on your research question, choose the texts that you will analyze. You need to decide:

• The medium (e.g. newspapers, speeches or websites) and genre (e.g. opinion pieces, political campaign speeches, or marketing copy)
• The inclusion and exclusion criteria (e.g. newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
• The parameters in terms of date range, location, etc.

If there are only a small amount of texts that meet your criteria, you might analyze all of them. If there is a large volume of texts, you can select a sample .

2. Define the units and categories of analysis

Next, you need to determine the level at which you will analyze your chosen texts. This means defining:

• The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
• The set of categories that you will use for coding. Categories can be objective characteristics (e.g. aged 30-40 ,  lawyer , parent ) or more conceptual (e.g. trustworthy , corrupt , conservative , family oriented ).

Your units of analysis are the politicians who appear in each article and the words and phrases that are used to describe them. Based on your research question, you have to categorize based on age and the concept of trustworthiness. To get more detailed data, you also code for other categories such as their political party and the marital status of each politician mentioned.

3. Develop a set of rules for coding

Coding involves organizing the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

In considering the category “younger politician,” you decide which titles will be coded with this category ( senator, governor, counselor, mayor ). With “trustworthy”, you decide which specific words or phrases related to trustworthiness (e.g. honest and reliable ) will be coded in this category.

4. Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti and Diction , which can help speed up the process of counting and categorizing words and phrases.

Following your coding rules, you examine each newspaper article in your sample. You record the characteristics of each politician mentioned, along with all words and phrases related to trustworthiness that are used to describe them.

5. Analyze the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context and audience of the texts.

Let’s say the results reveal that words and phrases related to trustworthiness appeared in the same sentence as an older politician more frequently than they did in the same sentence as a younger politician. From these results, you conclude that national newspapers present older politicians as more trustworthy than younger politicians, and infer that this might have an effect on readers’ perceptions of younger people in politics.

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Chapter 17. Content Analysis

Introduction.

Content analysis is a term that is used to mean both a method of data collection and a method of data analysis. Archival and historical works can be the source of content analysis, but so too can the contemporary media coverage of a story, blogs, comment posts, films, cartoons, advertisements, brand packaging, and photographs posted on Instagram or Facebook. Really, almost anything can be the “content” to be analyzed. This is a qualitative research method because the focus is on the meanings and interpretations of that content rather than strictly numerical counts or variables-based causal modeling. [1] Qualitative content analysis (sometimes referred to as QCA) is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest—in other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue. This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis. It is also a nice segue between our data collection methods (e.g., interviewing, observation) chapters and chapters 18 and 19, whose focus is on coding, the primary means of data analysis for most qualitative data. In many ways, the methods of content analysis are quite similar to the method of coding.

Although the body of material (“content”) to be collected and analyzed can be nearly anything, most qualitative content analysis is applied to forms of human communication (e.g., media posts, news stories, campaign speeches, advertising jingles). The point of the analysis is to understand this communication, to systematically and rigorously explore its meanings, assumptions, themes, and patterns. Historical and archival sources may be the subject of content analysis, but there are other ways to analyze (“code”) this data when not overly concerned with the communicative aspect (see chapters 18 and 19). This is why we tend to consider content analysis its own method of data collection as well as a method of data analysis. Still, many of the techniques you learn in this chapter will be helpful to any “coding” scheme you develop for other kinds of qualitative data. Just remember that content analysis is a particular form with distinct aims and goals and traditions.

An Overview of the Content Analysis Process

The first step: selecting content.

Figure 17.2 is a display of possible content for content analysis. The first step in content analysis is making smart decisions about what content you will want to analyze and to clearly connect this content to your research question or general focus of research. Why are you interested in the messages conveyed in this particular content? What will the identification of patterns here help you understand? Content analysis can be fun to do, but in order to make it research, you need to fit it into a research plan.

Figure 17.1. A Non-exhaustive List of "Content" for Content Analysis

To take one example, let us imagine you are interested in gender presentations in society and how presentations of gender have changed over time. There are various forms of content out there that might help you document changes. You could, for example, begin by creating a list of magazines that are coded as being for “women” (e.g., Women’s Daily Journal ) and magazines that are coded as being for “men” (e.g., Men’s Health ). You could then select a date range that is relevant to your research question (e.g., 1950s–1970s) and collect magazines from that era. You might create a “sample” by deciding to look at three issues for each year in the date range and a systematic plan for what to look at in those issues (e.g., advertisements? Cartoons? Titles of articles? Whole articles?). You are not just going to look at some magazines willy-nilly. That would not be systematic enough to allow anyone to replicate or check your findings later on. Once you have a clear plan of what content is of interest to you and what you will be looking at, you can begin, creating a record of everything you are including as your content. This might mean a list of each advertisement you look at or each title of stories in those magazines along with its publication date. You may decide to have multiple “content” in your research plan. For each content, you want a clear plan for collecting, sampling, and documenting.

The Second Step: Collecting and Storing

Once you have a plan, you are ready to collect your data. This may entail downloading from the internet, creating a Word document or PDF of each article or picture, and storing these in a folder designated by the source and date (e.g., “ Men’s Health advertisements, 1950s”). Sølvberg ( 2021 ), for example, collected posted job advertisements for three kinds of elite jobs (economic, cultural, professional) in Sweden. But collecting might also mean going out and taking photographs yourself, as in the case of graffiti, street signs, or even what people are wearing. Chaise LaDousa, an anthropologist and linguist, took photos of “house signs,” which are signs, often creative and sometimes offensive, hung by college students living in communal off-campus houses. These signs were a focal point of college culture, sending messages about the values of the students living in them. Some of the names will give you an idea: “Boot ’n Rally,” “The Plantation,” “Crib of the Rib.” The students might find these signs funny and benign, but LaDousa ( 2011 ) argued convincingly that they also reproduced racial and gender inequalities. The data here already existed—they were big signs on houses—but the researcher had to collect the data by taking photographs.

In some cases, your content will be in physical form but not amenable to photographing, as in the case of films or unwieldy physical artifacts you find in the archives (e.g., undigitized meeting minutes or scrapbooks). In this case, you need to create some kind of detailed log (fieldnotes even) of the content that you can reference. In the case of films, this might mean watching the film and writing down details for key scenes that become your data. [2] For scrapbooks, it might mean taking notes on what you are seeing, quoting key passages, describing colors or presentation style. As you might imagine, this can take a lot of time. Be sure you budget this time into your research plan.

Researcher Note

A note on data scraping : Data scraping, sometimes known as screen scraping or frame grabbing, is a way of extracting data generated by another program, as when a scraping tool grabs information from a website. This may help you collect data that is on the internet, but you need to be ethical in how to employ the scraper. A student once helped me scrape thousands of stories from the Time magazine archives at once (although it took several hours for the scraping process to complete). These stories were freely available, so the scraping process simply sped up the laborious process of copying each article of interest and saving it to my research folder. Scraping tools can sometimes be used to circumvent paywalls. Be careful here!

The Third Step: Analysis

There is often an assumption among novice researchers that once you have collected your data, you are ready to write about what you have found. Actually, you haven’t yet found anything, and if you try to write up your results, you will probably be staring sadly at a blank page. Between the collection and the writing comes the difficult task of systematically and repeatedly reviewing the data in search of patterns and themes that will help you interpret the data, particularly its communicative aspect (e.g., What is it that is being communicated here, with these “house signs” or in the pages of Men’s Health ?).

The first time you go through the data, keep an open mind on what you are seeing (or hearing), and take notes about your observations that link up to your research question. In the beginning, it can be difficult to know what is relevant and what is extraneous. Sometimes, your research question changes based on what emerges from the data. Use the first round of review to consider this possibility, but then commit yourself to following a particular focus or path. If you are looking at how gender gets made or re-created, don’t follow the white rabbit down a hole about environmental injustice unless you decide that this really should be the focus of your study or that issues of environmental injustice are linked to gender presentation. In the second round of review, be very clear about emerging themes and patterns. Create codes (more on these in chapters 18 and 19) that will help you simplify what you are noticing. For example, “men as outdoorsy” might be a common trope you see in advertisements. Whenever you see this, mark the passage or picture. In your third (or fourth or fifth) round of review, begin to link up the tropes you’ve identified, looking for particular patterns and assumptions. You’ve drilled down to the details, and now you are building back up to figure out what they all mean. Start thinking about theory—either theories you have read about and are using as a frame of your study (e.g., gender as performance theory) or theories you are building yourself, as in the Grounded Theory tradition. Once you have a good idea of what is being communicated and how, go back to the data at least one more time to look for disconfirming evidence. Maybe you thought “men as outdoorsy” was of importance, but when you look hard, you note that women are presented as outdoorsy just as often. You just hadn’t paid attention. It is very important, as any kind of researcher but particularly as a qualitative researcher, to test yourself and your emerging interpretations in this way.

The Fourth and Final Step: The Write-Up

Only after you have fully completed analysis, with its many rounds of review and analysis, will you be able to write about what you found. The interpretation exists not in the data but in your analysis of the data. Before writing your results, you will want to very clearly describe how you chose the data here and all the possible limitations of this data (e.g., historical-trace problem or power problem; see chapter 16). Acknowledge any limitations of your sample. Describe the audience for the content, and discuss the implications of this. Once you have done all of this, you can put forth your interpretation of the communication of the content, linking to theory where doing so would help your readers understand your findings and what they mean more generally for our understanding of how the social world works. [3]

Analyzing Content: Helpful Hints and Pointers

Although every data set is unique and each researcher will have a different and unique research question to address with that data set, there are some common practices and conventions. When reviewing your data, what do you look at exactly? How will you know if you have seen a pattern? How do you note or mark your data?

Let’s start with the last question first. If your data is stored digitally, there are various ways you can highlight or mark up passages. You can, of course, do this with literal highlighters, pens, and pencils if you have print copies. But there are also qualitative software programs to help you store the data, retrieve the data, and mark the data. This can simplify the process, although it cannot do the work of analysis for you.

Qualitative software can be very expensive, so the first thing to do is to find out if your institution (or program) has a universal license its students can use. If they do not, most programs have special student licenses that are less expensive. The two most used programs at this moment are probably ATLAS.ti and NVivo. Both can cost more than $500 [4] but provide everything you could possibly need for storing data, content analysis, and coding. They also have a lot of customer support, and you can find many official and unofficial tutorials on how to use the programs’ features on the web. Dedoose, created by academic researchers at UCLA, is a decent program that lacks many of the bells and whistles of the two big programs. Instead of paying all at once, you pay monthly, as you use the program. The monthly fee is relatively affordable (less than $15), so this might be a good option for a small project. HyperRESEARCH is another basic program created by academic researchers, and it is free for small projects (those that have limited cases and material to import). You can pay a monthly fee if your project expands past the free limits. I have personally used all four of these programs, and they each have their pluses and minuses.

Regardless of which program you choose, you should know that none of them will actually do the hard work of analysis for you. They are incredibly useful for helping you store and organize your data, and they provide abundant tools for marking, comparing, and coding your data so you can make sense of it. But making sense of it will always be your job alone.

So let’s say you have some software, and you have uploaded all of your content into the program: video clips, photographs, transcripts of news stories, articles from magazines, even digital copies of college scrapbooks. Now what do you do? What are you looking for? How do you see a pattern? The answers to these questions will depend partially on the particular research question you have, or at least the motivation behind your research. Let’s go back to the idea of looking at gender presentations in magazines from the 1950s to the 1970s. Here are some things you can look at and code in the content: (1) actions and behaviors, (2) events or conditions, (3) activities, (4) strategies and tactics, (5) states or general conditions, (6) meanings or symbols, (7) relationships/interactions, (8) consequences, and (9) settings. Table 17.1 lists these with examples from our gender presentation study.

Table 17.1. Examples of What to Note During Content Analysis

One thing to note about the examples in table 17.1: sometimes we note (mark, record, code) a single example, while other times, as in “settings,” we are recording a recurrent pattern. To help you spot patterns, it is useful to mark every setting, including a notation on gender. Using software can help you do this efficiently. You can then call up “setting by gender” and note this emerging pattern. There’s an element of counting here, which we normally think of as quantitative data analysis, but we are using the count to identify a pattern that will be used to help us interpret the communication. Content analyses often include counting as part of the interpretive (qualitative) process.

In your own study, you may not need or want to look at all of the elements listed in table 17.1. Even in our imagined example, some are more useful than others. For example, “strategies and tactics” is a bit of a stretch here. In studies that are looking specifically at, say, policy implementation or social movements, this category will prove much more salient.

Another way to think about “what to look at” is to consider aspects of your content in terms of units of analysis. You can drill down to the specific words used (e.g., the adjectives commonly used to describe “men” and “women” in your magazine sample) or move up to the more abstract level of concepts used (e.g., the idea that men are more rational than women). Counting for the purpose of identifying patterns is particularly useful here. How many times is that idea of women’s irrationality communicated? How is it is communicated (in comic strips, fictional stories, editorials, etc.)? Does the incidence of the concept change over time? Perhaps the “irrational woman” was everywhere in the 1950s, but by the 1970s, it is no longer showing up in stories and comics. By tracing its usage and prevalence over time, you might come up with a theory or story about gender presentation during the period. Table 17.2 provides more examples of using different units of analysis for this work along with suggestions for effective use.

Table 17.2. Examples of Unit of Analysis in Content Analysis

Every qualitative content analysis is unique in its particular focus and particular data used, so there is no single correct way to approach analysis. You should have a better idea, however, of what kinds of things to look for and what to look for. The next two chapters will take you further into the coding process, the primary analytical tool for qualitative research in general.

Further Readings

Cidell, Julie. 2010. “Content Clouds as Exploratory Qualitative Data Analysis.” Area 42(4):514–523. A demonstration of using visual “content clouds” as a form of exploratory qualitative data analysis using transcripts of public meetings and content of newspaper articles.

Hsieh, Hsiu-Fang, and Sarah E. Shannon. 2005. “Three Approaches to Qualitative Content Analysis.” Qualitative Health Research 15(9):1277–1288. Distinguishes three distinct approaches to QCA: conventional, directed, and summative. Uses hypothetical examples from end-of-life care research.

Jackson, Romeo, Alex C. Lange, and Antonio Duran. 2021. “A Whitened Rainbow: The In/Visibility of Race and Racism in LGBTQ Higher Education Scholarship.” Journal Committed to Social Change on Race and Ethnicity (JCSCORE) 7(2):174–206.* Using a “critical summative content analysis” approach, examines research published on LGBTQ people between 2009 and 2019.

Krippendorff, Klaus. 2018. Content Analysis: An Introduction to Its Methodology . 4th ed. Thousand Oaks, CA: SAGE. A very comprehensive textbook on both quantitative and qualitative forms of content analysis.

Mayring, Philipp. 2022. Qualitative Content Analysis: A Step-by-Step Guide . Thousand Oaks, CA: SAGE. Formulates an eight-step approach to QCA.

Messinger, Adam M. 2012. “Teaching Content Analysis through ‘Harry Potter.’” Teaching Sociology 40(4):360–367. This is a fun example of a relatively brief foray into content analysis using the music found in Harry Potter films.

Neuendorft, Kimberly A. 2002. The Content Analysis Guidebook . Thousand Oaks, CA: SAGE. Although a helpful guide to content analysis in general, be warned that this textbook definitely favors quantitative over qualitative approaches to content analysis.

Schrier, Margrit. 2012. Qualitative Content Analysis in Practice . Thousand Okas, CA: SAGE. Arguably the most accessible guidebook for QCA, written by a professor based in Germany.

Weber, Matthew A., Shannon Caplan, Paul Ringold, and Karen Blocksom. 2017. “Rivers and Streams in the Media: A Content Analysis of Ecosystem Services.” Ecology and Society 22(3).* Examines the content of a blog hosted by National Geographic and articles published in The New York Times and the Wall Street Journal for stories on rivers and streams (e.g., water-quality flooding).

• There are ways of handling content analysis quantitatively, however. Some practitioners therefore specify qualitative content analysis (QCA). In this chapter, all content analysis is QCA unless otherwise noted. ↵
• Note that some qualitative software allows you to upload whole films or film clips for coding. You will still have to get access to the film, of course. ↵
• See chapter 20 for more on the final presentation of research. ↵
• . Actually, ATLAS.ti is an annual license, while NVivo is a perpetual license, but both are going to cost you at least $500 to use. Student rates may be lower. And don’t forget to ask your institution or program if they already have a software license you can use. ↵

A method of both data collection and data analysis in which a given content (textual, visual, graphic) is examined systematically and rigorously to identify meanings, themes, patterns and assumptions.  Qualitative content analysis (QCA) is concerned with gathering and interpreting an existing body of material.    

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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A hands-on guide to doing content analysis

Christen erlingsson.

a Department of Health and Caring Sciences, Linnaeus University, Kalmar 391 82, Sweden

Petra Brysiewicz

b School of Nursing & Public Health, University of KwaZulu-Natal, Durban 4041, South Africa

Associated Data

There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including the emergency care context in Africa. Novice qualitative researchers are often daunted by the prospect of qualitative data analysis and thus may experience much difficulty in the data analysis process. Our objective with this manuscript is to provide a practical hands-on example of qualitative content analysis to aid novice qualitative researchers in their task.

African relevance

• • Qualitative research is useful to deepen the understanding of the human experience.
• • Novice qualitative researchers may benefit from this hands-on guide to content analysis.
• • Practical tips and data analysis templates are provided to assist in the analysis process.

Introduction

There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including emergency care research. An increasing number of health researchers are currently opting to use various qualitative research approaches in exploring and describing complex phenomena, providing textual accounts of individuals’ “life worlds”, and giving voice to vulnerable populations our patients so often represent. Many articles and books are available that describe qualitative research methods and provide overviews of content analysis procedures [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] . Some articles include step-by-step directions intended to clarify content analysis methodology. What we have found in our teaching experience is that these directions are indeed very useful. However, qualitative researchers, especially novice researchers, often struggle to understand what is happening on and between steps, i.e., how the steps are taken.

As research supervisors of postgraduate health professionals, we often meet students who present brilliant ideas for qualitative studies that have potential to fill current gaps in the literature. Typically, the suggested studies aim to explore human experience. Research questions exploring human experience are expediently studied through analysing textual data e.g., collected in individual interviews, focus groups, documents, or documented participant observation. When reflecting on the proposed study aim together with the student, we often suggest content analysis methodology as the best fit for the study and the student, especially the novice researcher. The interview data are collected and the content analysis adventure begins. Students soon realise that data based on human experiences are complex, multifaceted and often carry meaning on multiple levels.

For many novice researchers, analysing qualitative data is found to be unexpectedly challenging and time-consuming. As they soon discover, there is no step-wise analysis process that can be applied to the data like a pattern cutter at a textile factory. They may become extremely annoyed and frustrated during the hands-on enterprise of qualitative content analysis.

The novice researcher may lament, “I’ve read all the methodology but don’t really know how to start and exactly what to do with my data!” They grapple with qualitative research terms and concepts, for example; differences between meaning units, codes, categories and themes, and regarding increasing levels of abstraction from raw data to categories or themes. The content analysis adventure may now seem to be a chaotic undertaking. But, life is messy, complex and utterly fascinating. Experiencing chaos during analysis is normal. Good advice for the qualitative researcher is to be open to the complexity in the data and utilise one’s flow of creativity.

Inspired primarily by descriptions of “conventional content analysis” in Hsieh and Shannon [3] , “inductive content analysis” in Elo and Kyngäs [5] and “qualitative content analysis of an interview text” in Graneheim and Lundman [1] , we have written this paper to help the novice qualitative researcher navigate the uncertainty in-between the steps of qualitative content analysis. We will provide advice and practical tips, as well as data analysis templates, to attempt to ease frustration and hopefully, inspire readers to discover how this exciting methodology contributes to developing a deeper understanding of human experience and our professional contexts.

Overview of qualitative content analysis

Synopsis of content analysis.

A common starting point for qualitative content analysis is often transcribed interview texts. The objective in qualitative content analysis is to systematically transform a large amount of text into a highly organised and concise summary of key results. Analysis of the raw data from verbatim transcribed interviews to form categories or themes is a process of further abstraction of data at each step of the analysis; from the manifest and literal content to latent meanings ( Fig. 1 and Table 1 ).

Example of analysis leading to higher levels of abstraction; from manifest to latent content.

Glossary of terms as used in this hands-on guide to doing content analysis. *

The initial step is to read and re-read the interviews to get a sense of the whole, i.e., to gain a general understanding of what your participants are talking about. At this point you may already start to get ideas of what the main points or ideas are that your participants are expressing. Then one needs to start dividing up the text into smaller parts, namely, into meaning units. One then condenses these meaning units further. While doing this, you need to ensure that the core meaning is still retained. The next step is to label condensed meaning units by formulating codes and then grouping these codes into categories. Depending on the study’s aim and quality of the collected data, one may choose categories as the highest level of abstraction for reporting results or you can go further and create themes [1] , [2] , [3] , [5] , [8] .

Content analysis as a reflective process

You must mould the clay of the data , tapping into your intuition while maintaining a reflective understanding of how your own previous knowledge is influencing your analysis, i.e., your pre-understanding. In qualitative methodology, it is imperative to vigilantly maintain an awareness of one’s pre-understanding so that this does not influence analysis and/or results. This is the difficult balancing task of keeping a firm grip on one’s assumptions, opinions, and personal beliefs, and not letting them unconsciously steer your analysis process while simultaneously, and knowingly, utilising one’s pre-understanding to facilitate a deeper understanding of the data.

Content analysis, as in all qualitative analysis, is a reflective process. There is no “step 1, 2, 3, done!” linear progression in the analysis. This means that identifying and condensing meaning units, coding, and categorising are not one-time events. It is a continuous process of coding and categorising then returning to the raw data to reflect on your initial analysis. Are you still satisfied with the length of meaning units? Do the condensed meaning units and codes still “fit” with each other? Do the codes still fit into this particular category? Typically, a fair amount of adjusting is needed after the first analysis endeavour. For example: a meaning unit might need to be split into two meaning units in order to capture an additional core meaning; a code modified to more closely match the core meaning of the condensed meaning unit; or a category name tweaked to most accurately describe the included codes. In other words, analysis is a flexible reflective process of working and re-working your data that reveals connections and relationships. Once condensed meaning units are coded it is easier to get a bigger picture and see patterns in your codes and organise codes in categories.

Content analysis exercise

The synopsis above is representative of analysis descriptions in many content analysis articles. Although correct, such method descriptions still do not provide much support for the novice researcher during the actual analysis process. Aspiring to provide guidance and direction to support the novice, a practical example of doing the actual work of content analysis is provided in the following sections. This practical example is based on a transcribed interview excerpt that was part of a study that aimed to explore patients’ experiences of being admitted into the emergency centre ( Fig. 2 ).

Excerpt from interview text exploring “Patient’s experience of being admitted into the emergency centre”

This content analysis exercise provides instructions, tips, and advice to support the content analysis novice in a) familiarising oneself with the data and the hermeneutic spiral, b) dividing up the text into meaning units and subsequently condensing these meaning units, c) formulating codes, and d) developing categories and themes.

Familiarising oneself with the data and the hermeneutic spiral

An important initial phase in the data analysis process is to read and re-read the transcribed interview while keeping your aim in focus. Write down your initial impressions. Embrace your intuition. What is the text talking about? What stands out? How did you react while reading the text? What message did the text leave you with? In this analysis phase, you are gaining a sense of the text as a whole.

You may ask why this is important. During analysis, you will be breaking down the whole text into smaller parts. Returning to your notes with your initial impressions will help you see if your “parts” analysis is matching up with your first impressions of the “whole” text. Are your initial impressions visible in your analysis of the parts? Perhaps you need to go back and check for different perspectives. This is what is referred to as the hermeneutic spiral or hermeneutic circle. It is the process of comparing the parts to the whole to determine whether impressions of the whole verify the analysis of the parts in all phases of analysis. Each part should reflect the whole and the whole should be reflected in each part. This concept will become clearer as you start working with your data.

Dividing up the text into meaning units and condensing meaning units

You have now read the interview a number of times. Keeping your research aim and question clearly in focus, divide up the text into meaning units. Located meaning units are then condensed further while keeping the central meaning intact ( Table 2 ). The condensation should be a shortened version of the same text that still conveys the essential message of the meaning unit. Sometimes the meaning unit is already so compact that no further condensation is required. Some content analysis sources warn researchers against short meaning units, claiming that this can lead to fragmentation [1] . However, our personal experience as research supervisors has shown us that a greater problem for the novice is basing analysis on meaning units that are too large and include many meanings which are then lost in the condensation process.

Suggestion for how the exemplar interview text can be divided into meaning units and condensed meaning units ( condensations are in parentheses ).

Formulating codes

The next step is to develop codes that are descriptive labels for the condensed meaning units ( Table 3 ). Codes concisely describe the condensed meaning unit and are tools to help researchers reflect on the data in new ways. Codes make it easier to identify connections between meaning units. At this stage of analysis you are still keeping very close to your data with very limited interpretation of content. You may adjust, re-do, re-think, and re-code until you get to the point where you are satisfied that your choices are reasonable. Just as in the initial phase of getting to know your data as a whole, it is also good to write notes during coding on your impressions and reactions to the text.

Suggestions for coding of condensed meaning units.

Developing categories and themes

The next step is to sort codes into categories that answer the questions who , what , when or where? One does this by comparing codes and appraising them to determine which codes seem to belong together, thereby forming a category. In other words, a category consists of codes that appear to deal with the same issue, i.e., manifest content visible in the data with limited interpretation on the part of the researcher. Category names are most often short and factual sounding.

In data that is rich with latent meaning, analysis can be carried on to create themes. In our practical example, we have continued the process of abstracting data to a higher level, from category to theme level, and developed three themes as well as an overarching theme ( Table 4 ). Themes express underlying meaning, i.e., latent content, and are formed by grouping two or more categories together. Themes are answering questions such as why , how , in what way or by what means? Therefore, theme names include verbs, adverbs and adjectives and are very descriptive or even poetic.

Suggestion for organisation of coded meaning units into categories and themes.

Some reflections and helpful tips

Understand your pre-understandings.

While conducting qualitative research, it is paramount that the researcher maintains a vigilance of non-bias during analysis. In other words, did you remain aware of your pre-understandings, i.e., your own personal assumptions, professional background, and previous experiences and knowledge? For example, did you zero in on particular aspects of the interview on account of your profession (as an emergency doctor, emergency nurse, pre-hospital professional, etc.)? Did you assume the patient’s gender? Did your assumptions affect your analysis? How about aspects of culpability; did you assume that this patient was at fault or that this patient was a victim in the crash? Did this affect how you analysed the text?

Staying aware of one’s pre-understandings is exactly as difficult as it sounds. But, it is possible and it is requisite. Focus on putting yourself and your pre-understandings in a holding pattern while you approach your data with an openness and expectation of finding new perspectives. That is the key: expect the new and be prepared to be surprised. If something in your data feels unusual, is different from what you know, atypical, or even odd – don’t by-pass it as “wrong”. Your reactions and intuitive responses are letting you know that here is something to pay extra attention to, besides the more comfortable condensing and coding of more easily recognisable meaning units.

Use your intuition

Intuition is a great asset in qualitative analysis and not to be dismissed as “unscientific”. Intuition results from tacit knowledge. Just as tacit knowledge is a hallmark of great clinicians [11] , [12] ; it is also an invaluable tool in analysis work [13] . Literally, take note of your gut reactions and intuitive guidance and remember to write these down! These notes often form a framework of possible avenues for further analysis and are especially helpful as you lift the analysis to higher levels of abstraction; from meaning units to condensed meaning units, to codes, to categories and then to the highest level of abstraction in content analysis, themes.

Aspects of coding and categorising hard to place data

All too often, the novice gets overwhelmed by interview material that deals with the general subject matter of the interview, but doesn’t seem to answer the research question. Don’t be too quick to consider such text as off topic or dross [6] . There is often data that, although not seeming to match the study aim precisely, is still important for illuminating the problem area. This can be seen in our practical example about exploring patients’ experiences of being admitted into the emergency centre. Initially the participant is describing the accident itself. While not directly answering the research question, the description is important for understanding the context of the experience of being admitted into the emergency centre. It is very common that participants will “begin at the beginning” and prologue their narratives in order to create a context that sets the scene. This type of contextual data is vital for gaining a deepened understanding of participants’ experiences.

In our practical example, the participant begins by describing the crash and the rescue, i.e., experiences leading up to and prior to admission to the emergency centre. That is why we have chosen in our analysis to code the condensed meaning unit “Ambulance staff looked worried about all the blood” as “In the ambulance” and place it in the category “Reliving the rescue”. We did not choose to include this meaning unit in the categories specifically about admission to the emergency centre itself. Do you agree with our coding choice? Would you have chosen differently?

Another common problem for the novice is deciding how to code condensed meaning units when the unit can be labelled in several different ways. At this point researchers usually groan and wish they had thought to ask one of those classic follow-up questions like “Can you tell me a little bit more about that?” We have examples of two such coding conundrums in the exemplar, as can be seen in Table 3 (codes we conferred on) and Table 4 (codes we reached consensus on). Do you agree with our choices or would you have chosen different codes? Our best advice is to go back to your impressions of the whole and lean into your intuition when choosing codes that are most reasonable and best fit your data.

A typical problem area during categorisation, especially for the novice researcher, is overlap between content in more than one initial category, i.e., codes included in one category also seem to be a fit for another category. Overlap between initial categories is very likely an indication that the jump from code to category was too big, a problem not uncommon when the data is voluminous and/or very complex. In such cases, it can be helpful to first sort codes into narrower categories, so-called subcategories. Subcategories can then be reviewed for possibilities of further aggregation into categories. In the case of a problematic coding, it is advantageous to return to the meaning unit and check if the meaning unit itself fits the category or if you need to reconsider your preliminary coding.

It is not uncommon to be faced by thorny problems such as these during coding and categorisation. Here we would like to reiterate how valuable it is to have fellow researchers with whom you can discuss and reflect together with, in order to reach consensus on the best way forward in your data analysis. It is really advantageous to compare your analysis with meaning units, condensations, coding and categorisations done by another researcher on the same text. Have you identified the same meaning units? Do you agree on coding? See similar patterns in the data? Concur on categories? Sometimes referred to as “researcher triangulation,” this is actually a key element in qualitative analysis and an important component when striving to ensure trustworthiness in your study [14] . Qualitative research is about seeking out variations and not controlling variables, as in quantitative research. Collaborating with others during analysis lets you tap into multiple perspectives and often makes it easier to see variations in the data, thereby enhancing the quality of your results as well as contributing to the rigor of your study. It is important to note that it is not necessary to force consensus in the findings but one can embrace these variations in interpretation and use that to capture the richness in the data.

Yet there are times when neither openness, pre-understanding, intuition, nor researcher triangulation does the job; for example, when analysing an interview and one is simply confused on how to code certain meaning units. At such times, there are a variety of options. A good starting place is to re-read all the interviews through the lens of this specific issue and actively search for other similar types of meaning units you might have missed. Another way to handle this is to conduct further interviews with specific queries that hopefully shed light on the issue. A third option is to have a follow-up interview with the same person and ask them to explain.

Additional tips

It is important to remember that in a typical project there are several interviews to analyse. Codes found in a single interview serve as a starting point as you then work through the remaining interviews coding all material. Form your categories and themes when all project interviews have been coded.

When submitting an article with your study results, it is a good idea to create a table or figure providing a few key examples of how you progressed from the raw data of meaning units, to condensed meaning units, coding, categorisation, and, if included, themes. Providing such a table or figure supports the rigor of your study [1] and is an element greatly appreciated by reviewers and research consumers.

During the analysis process, it can be advantageous to write down your research aim and questions on a sheet of paper that you keep nearby as you work. Frequently referring to your aim can help you keep focused and on track during analysis. Many find it helpful to colour code their transcriptions and write notes in the margins.

Having access to qualitative analysis software can be greatly helpful in organising and retrieving analysed data. Just remember, a computer does not analyse the data. As Jennings [15] has stated, “… it is ‘peopleware,’ not software, that analyses.” A major drawback is that qualitative analysis software can be prohibitively expensive. One way forward is to use table templates such as we have used in this article. (Three analysis templates, Templates A, B, and C, are provided as supplementary online material ). Additionally, the “find” function in word processing programmes such as Microsoft Word (Redmond, WA USA) facilitates locating key words, e.g., in transcribed interviews, meaning units, and codes.

Lessons learnt/key points

From our experience with content analysis we have learnt a number of important lessons that may be useful for the novice researcher. They are:

• • A method description is a guideline supporting analysis and trustworthiness. Don’t get caught up too rigidly following steps. Reflexivity and flexibility are just as important. Remember that a method description is a tool helping you in the process of making sense of your data by reducing a large amount of text to distil key results.
• • It is important to maintain a vigilant awareness of one’s own pre-understandings in order to avoid bias during analysis and in results.
• • Use and trust your own intuition during the analysis process.
• • If possible, discuss and reflect together with other researchers who have analysed the same data. Be open and receptive to new perspectives.
• • Understand that it is going to take time. Even if you are quite experienced, each set of data is different and all require time to analyse. Don’t expect to have all the data analysis done over a weekend. It may take weeks. You need time to think, reflect and then review your analysis.
• • Keep reminding yourself how excited you have felt about this area of research and how interesting it is. Embrace it with enthusiasm!
• • Let it be chaotic – have faith that some sense will start to surface. Don’t be afraid and think you will never get to the end – you will… eventually!

Peer review under responsibility of African Federation for Emergency Medicine.

Appendix A Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.afjem.2017.08.001 .

Appendix A. Supplementary data

What Is Qualitative Content Analysis?

Qca explained simply (with examples).

By: Jenna Crosley (PhD). Reviewed by: Dr Eunice Rautenbach (DTech) | February 2021

If you’re in the process of preparing for your dissertation, thesis or research project, you’ve probably encountered the term “ qualitative content analysis ” – it’s quite a mouthful. If you’ve landed on this post, you’re probably a bit confused about it. Well, the good news is that you’ve come to the right place…

Overview: Qualitative Content Analysis

• What (exactly) is qualitative content analysis
• The two main types of content analysis
• When to use content analysis
• How to conduct content analysis (the process)
• The advantages and disadvantages of content analysis

1. What is content analysis?

Content analysis is a  qualitative analysis method  that focuses on recorded human artefacts such as manuscripts, voice recordings and journals. Content analysis investigates these written, spoken and visual artefacts without explicitly extracting data from participants – this is called  unobtrusive  research.

In other words, with content analysis, you don’t necessarily need to interact with participants (although you can if necessary); you can simply analyse the data that they have already produced. With this type of analysis, you can analyse data such as text messages, books, Facebook posts, videos, and audio (just to mention a few).

The basics – explicit and implicit content

When working with content analysis, explicit and implicit content will play a role. Explicit data is transparent and easy to identify, while implicit data is that which requires some form of interpretation and is often of a subjective nature. Sounds a bit fluffy? Here’s an example:

Joe: Hi there, what can I help you with? 

Lauren: I recently adopted a puppy and I’m worried that I’m not feeding him the right food. Could you please advise me on what I should be feeding? 

Joe: Sure, just follow me and I’ll show you. Do you have any other pets?

Lauren: Only one, and it tweets a lot!

In this exchange, the explicit data indicates that Joe is helping Lauren to find the right puppy food. Lauren asks Joe whether she has any pets aside from her puppy. This data is explicit because it requires no interpretation.

On the other hand, implicit data , in this case, includes the fact that the speakers are in a pet store. This information is not clearly stated but can be inferred from the conversation, where Joe is helping Lauren to choose pet food. An additional piece of implicit data is that Lauren likely has some type of bird as a pet. This can be inferred from the way that Lauren states that her pet “tweets”.

As you can see, explicit and implicit data both play a role in human interaction  and are an important part of your analysis. However, it’s important to differentiate between these two types of data when you’re undertaking content analysis. Interpreting implicit data can be rather subjective as conclusions are based on the researcher’s interpretation. This can introduce an element of bias , which risks skewing your results.

2. The two types of content analysis

Now that you understand the difference between implicit and explicit data, let’s move on to the two general types of content analysis : conceptual and relational content analysis. Importantly, while conceptual and relational content analysis both follow similar steps initially, the aims and outcomes of each are different.

Conceptual analysis focuses on the number of times a concept occurs in a set of data and is generally focused on explicit data. For example, if you were to have the following conversation:

Marie: She told me that she has three cats.

Jean: What are her cats’ names?

Marie: I think the first one is Bella, the second one is Mia, and… I can’t remember the third cat’s name.

In this data, you can see that the word “cat” has been used three times. Through conceptual content analysis, you can deduce that cats are the central topic of the conversation. You can also perform a frequency analysis , where you assess the term’s frequency in the data. For example, in the exchange above, the word “cat” makes up 9% of the data. In other words, conceptual analysis brings a little bit of quantitative analysis into your qualitative analysis.

As you can see, the above data is without interpretation and focuses on explicit data . Relational content analysis, on the other hand, takes a more holistic view by focusing more on implicit data in terms of context, surrounding words and relationships.

There are three types of relational analysis:

• Affect extraction
• Proximity analysis
• Cognitive mapping

Affect extraction is when you assess concepts according to emotional attributes. These emotions are typically mapped on scales, such as a Likert scale or a rating scale ranging from 1 to 5, where 1 is “very sad” and 5 is “very happy”.

If participants are talking about their achievements, they are likely to be given a score of 4 or 5, depending on how good they feel about it. If a participant is describing a traumatic event, they are likely to have a much lower score, either 1 or 2.

Proximity analysis identifies explicit terms (such as those found in a conceptual analysis) and the patterns in terms of how they co-occur in a text. In other words, proximity analysis investigates the relationship between terms and aims to group these to extract themes and develop meaning.

Proximity analysis is typically utilised when you’re looking for hard facts rather than emotional, cultural, or contextual factors. For example, if you were to analyse a political speech, you may want to focus only on what has been said, rather than implications or hidden meanings. To do this, you would make use of explicit data, discounting any underlying meanings and implications of the speech.

Lastly, there’s cognitive mapping, which can be used in addition to, or along with, proximity analysis. Cognitive mapping involves taking different texts and comparing them in a visual format – i.e. a cognitive map. Typically, you’d use cognitive mapping in studies that assess changes in terms, definitions, and meanings over time. It can also serve as a way to visualise affect extraction or proximity analysis and is often presented in a form such as a graphic map.

To recap on the essentials, content analysis is a qualitative analysis method that focuses on recorded human artefacts . It involves both conceptual analysis (which is more numbers-based) and relational analysis (which focuses on the relationships between concepts and how they’re connected).

Need a helping hand?

3. When should you use content analysis?

Content analysis is a useful tool that provides insight into trends of communication . For example, you could use a discussion forum as the basis of your analysis and look at the types of things the members talk about as well as how they use language to express themselves. Content analysis is flexible in that it can be applied to the individual, group, and institutional level.

Content analysis is typically used in studies where the aim is to better understand factors such as behaviours, attitudes, values, emotions, and opinions . For example, you could use content analysis to investigate an issue in society, such as miscommunication between cultures. In this example, you could compare patterns of communication in participants from different cultures, which will allow you to create strategies for avoiding misunderstandings in intercultural interactions.

Another example could include conducting content analysis on a publication such as a book. Here you could gather data on the themes, topics, language use and opinions reflected in the text to draw conclusions regarding the political (such as conservative or liberal) leanings of the publication.

4. How to conduct a qualitative content analysis

Conceptual and relational content analysis differ in terms of their exact process ; however, there are some similarities. Let’s have a look at these first – i.e., the generic process:

• Recap on your research questions
• Undertake bracketing to identify biases
• Operationalise your variables and develop a coding scheme
• Code the data and undertake your analysis

Step 1 – Recap on your research questions

It’s always useful to begin a project with research questions , or at least with an idea of what you are looking for. In fact, if you’ve spent time reading this blog, you’ll know that it’s useful to recap on your research questions, aims and objectives when undertaking pretty much any research activity. In the context of content analysis, it’s difficult to know what needs to be coded and what doesn’t, without a clear view of the research questions.

For example, if you were to code a conversation focused on basic issues of social justice, you may be met with a wide range of topics that may be irrelevant to your research. However, if you approach this data set with the specific intent of investigating opinions on gender issues, you will be able to focus on this topic alone, which would allow you to code only what you need to investigate.

Step 2 – Reflect on your personal perspectives and biases

It’s vital that you reflect on your own pre-conception of the topic at hand and identify the biases that you might drag into your content analysis – this is called “ bracketing “. By identifying this upfront, you’ll be more aware of them and less likely to have them subconsciously influence your analysis.

For example, if you were to investigate how a community converses about unequal access to healthcare, it is important to assess your views to ensure that you don’t project these onto your understanding of the opinions put forth by the community. If you have access to medical aid, for instance, you should not allow this to interfere with your examination of unequal access.

Step 3 – Operationalise your variables and develop a coding scheme

Next, you need to operationalise your variables . But what does that mean? Simply put, it means that you have to define each variable or construct . Give every item a clear definition – what does it mean (include) and what does it not mean (exclude). For example, if you were to investigate children’s views on healthy foods, you would first need to define what age group/range you’re looking at, and then also define what you mean by “healthy foods”.

In combination with the above, it is important to create a coding scheme , which will consist of information about your variables (how you defined each variable), as well as a process for analysing the data. For this, you would refer back to how you operationalised/defined your variables so that you know how to code your data.

For example, when coding, when should you code a food as “healthy”? What makes a food choice healthy? Is it the absence of sugar or saturated fat? Is it the presence of fibre and protein? It’s very important to have clearly defined variables to achieve consistent coding – without this, your analysis will get very muddy, very quickly.

Step 4 – Code and analyse the data

The next step is to code the data. At this stage, there are some differences between conceptual and relational analysis.

As described earlier in this post, conceptual analysis looks at the existence and frequency of concepts, whereas a relational analysis looks at the relationships between concepts. For both types of analyses, it is important to pre-select a concept that you wish to assess in your data. Using the example of studying children’s views on healthy food, you could pre-select the concept of “healthy food” and assess the number of times the concept pops up in your data.

Here is where conceptual and relational analysis start to differ.

At this stage of conceptual analysis , it is necessary to decide on the level of analysis you’ll perform on your data, and whether this will exist on the word, phrase, sentence, or thematic level. For example, will you code the phrase “healthy food” on its own? Will you code each term relating to healthy food (e.g., broccoli, peaches, bananas, etc.) with the code “healthy food” or will these be coded individually? It is very important to establish this from the get-go to avoid inconsistencies that could result in you having to code your data all over again.

On the other hand, relational analysis looks at the type of analysis. So, will you use affect extraction? Proximity analysis? Cognitive mapping? A mix? It’s vital to determine the type of analysis before you begin to code your data so that you can maintain the reliability and validity of your research .

How to conduct conceptual analysis

First, let’s have a look at the process for conceptual analysis.

Once you’ve decided on your level of analysis, you need to establish how you will code your concepts, and how many of these you want to code. Here you can choose whether you want to code in a deductive or inductive manner. Just to recap, deductive coding is when you begin the coding process with a set of pre-determined codes, whereas inductive coding entails the codes emerging as you progress with the coding process. Here it is also important to decide what should be included and excluded from your analysis, and also what levels of implication you wish to include in your codes.

For example, if you have the concept of “tall”, can you include “up in the clouds”, derived from the sentence, “the giraffe’s head is up in the clouds” in the code, or should it be a separate code? In addition to this, you need to know what levels of words may be included in your codes or not. For example, if you say, “the panda is cute” and “look at the panda’s cuteness”, can “cute” and “cuteness” be included under the same code?

Once you’ve considered the above, it’s time to code the text . We’ve already published a detailed post about coding , so we won’t go into that process here. Once you’re done coding, you can move on to analysing your results. This is where you will aim to find generalisations in your data, and thus draw your conclusions .

How to conduct relational analysis

Now let’s return to relational analysis.

As mentioned, you want to look at the relationships between concepts . To do this, you’ll need to create categories by reducing your data (in other words, grouping similar concepts together) and then also code for words and/or patterns. These are both done with the aim of discovering whether these words exist, and if they do, what they mean.

Your next step is to assess your data and to code the relationships between your terms and meanings, so that you can move on to your final step, which is to sum up and analyse the data.

To recap, it’s important to start your analysis process by reviewing your research questions and identifying your biases . From there, you need to operationalise your variables, code your data and then analyse it.

5. What are the pros & cons of content analysis?

One of the main advantages of content analysis is that it allows you to use a mix of quantitative and qualitative research methods, which results in a more scientifically rigorous analysis.

For example, with conceptual analysis, you can count the number of times that a term or a code appears in a dataset, which can be assessed from a quantitative standpoint. In addition to this, you can then use a qualitative approach to investigate the underlying meanings of these and relationships between them.

Content analysis is also unobtrusive and therefore poses fewer ethical issues than some other analysis methods. As the content you’ll analyse oftentimes already exists, you’ll analyse what has been produced previously, and so you won’t have to collect data directly from participants. When coded correctly, data is analysed in a very systematic and transparent manner, which means that issues of replicability (how possible it is to recreate research under the same conditions) are reduced greatly.

On the downside , qualitative research (in general, not just content analysis) is often critiqued for being too subjective and for not being scientifically rigorous enough. This is where reliability (how replicable a study is by other researchers) and validity (how suitable the research design is for the topic being investigated) come into play – if you take these into account, you’ll be on your way to achieving sound research results.

Recap: Qualitative content analysis

In this post, we’ve covered a lot of ground – click on any of the sections to recap:

If you have any questions about qualitative content analysis, feel free to leave a comment below. If you’d like 1-on-1 help with your qualitative content analysis, be sure to book an initial consultation with one of our friendly Research Coaches.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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18 Comments

If I am having three pre-decided attributes for my research based on which a set of semi-structured questions where asked then should I conduct a conceptual content analysis or relational content analysis. please note that all three attributes are different like Agility, Resilience and AI.

Thank you very much. I really enjoyed every word.

please send me one/ two sample of content analysis

send me to any sample of qualitative content analysis as soon as possible

Many thanks for the brilliant explanation. Do you have a sample practical study of a foreign policy using content analysis?

1) It will be very much useful if a small but complete content analysis can be sent, from research question to coding and analysis. 2) Is there any software by which qualitative content analysis can be done?

Common software for qualitative analysis is nVivo, and quantitative analysis is IBM SPSS

Thank you. Can I have at least 2 copies of a sample analysis study as my reference?

Could you please send me some sample of textbook content analysis?

Can I send you my research topic, aims, objectives and questions to give me feedback on them?

please could you send me samples of content analysis?

Yes please send

really we enjoyed your knowledge thanks allot. from Ethiopia

can you please share some samples of content analysis(relational)? I am a bit confused about processing the analysis part

Is it possible for you to list the journal articles and books or other sources you used to write this article? Thank you.

can you please send some samples of content analysis ?

can you kindly send some good examples done by using content analysis ?

This was very useful. can you please send me sample for qualitative content analysis. thank you

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Home » Content Analysis – Methods, Types and Examples

Content Analysis – Methods, Types and Examples

Table of Contents

Content Analysis

Definition:

Content analysis is a research method used to analyze and interpret the characteristics of various forms of communication, such as text, images, or audio. It involves systematically analyzing the content of these materials, identifying patterns, themes, and other relevant features, and drawing inferences or conclusions based on the findings.

Content analysis can be used to study a wide range of topics, including media coverage of social issues, political speeches, advertising messages, and online discussions, among others. It is often used in qualitative research and can be combined with other methods to provide a more comprehensive understanding of a particular phenomenon.

Types of Content Analysis

There are generally two types of content analysis:

Quantitative Content Analysis

This type of content analysis involves the systematic and objective counting and categorization of the content of a particular form of communication, such as text or video. The data obtained is then subjected to statistical analysis to identify patterns, trends, and relationships between different variables. Quantitative content analysis is often used to study media content, advertising, and political speeches.

Qualitative Content Analysis

This type of content analysis is concerned with the interpretation and understanding of the meaning and context of the content. It involves the systematic analysis of the content to identify themes, patterns, and other relevant features, and to interpret the underlying meanings and implications of these features. Qualitative content analysis is often used to study interviews, focus groups, and other forms of qualitative data, where the researcher is interested in understanding the subjective experiences and perceptions of the participants.

Methods of Content Analysis

There are several methods of content analysis, including:

Conceptual Analysis

This method involves analyzing the meanings of key concepts used in the content being analyzed. The researcher identifies key concepts and analyzes how they are used, defining them and categorizing them into broader themes.

Content Analysis by Frequency

This method involves counting and categorizing the frequency of specific words, phrases, or themes that appear in the content being analyzed. The researcher identifies relevant keywords or phrases and systematically counts their frequency.

Comparative Analysis

This method involves comparing the content of two or more sources to identify similarities, differences, and patterns. The researcher selects relevant sources, identifies key themes or concepts, and compares how they are represented in each source.

Discourse Analysis

This method involves analyzing the structure and language of the content being analyzed to identify how the content constructs and represents social reality. The researcher analyzes the language used and the underlying assumptions, beliefs, and values reflected in the content.

Narrative Analysis

This method involves analyzing the content as a narrative, identifying the plot, characters, and themes, and analyzing how they relate to the broader social context. The researcher identifies the underlying messages conveyed by the narrative and their implications for the broader social context.

Content Analysis Conducting Guide

Here is a basic guide to conducting a content analysis:

• Define your research question or objective: Before starting your content analysis, you need to define your research question or objective clearly. This will help you to identify the content you need to analyze and the type of analysis you need to conduct.
• Select your sample: Select a representative sample of the content you want to analyze. This may involve selecting a random sample, a purposive sample, or a convenience sample, depending on the research question and the availability of the content.
• Develop a coding scheme: Develop a coding scheme or a set of categories to use for coding the content. The coding scheme should be based on your research question or objective and should be reliable, valid, and comprehensive.
• Train coders: Train coders to use the coding scheme and ensure that they have a clear understanding of the coding categories and procedures. You may also need to establish inter-coder reliability to ensure that different coders are coding the content consistently.
• Code the content: Code the content using the coding scheme. This may involve manually coding the content, using software, or a combination of both.
• Analyze the data: Once the content is coded, analyze the data using appropriate statistical or qualitative methods, depending on the research question and the type of data.
• Interpret the results: Interpret the results of the analysis in the context of your research question or objective. Draw conclusions based on the findings and relate them to the broader literature on the topic.
• Report your findings: Report your findings in a clear and concise manner, including the research question, methodology, results, and conclusions. Provide details about the coding scheme, inter-coder reliability, and any limitations of the study.

Applications of Content Analysis

Content analysis has numerous applications across different fields, including:

• Media Research: Content analysis is commonly used in media research to examine the representation of different groups, such as race, gender, and sexual orientation, in media content. It can also be used to study media framing, media bias, and media effects.
• Political Communication : Content analysis can be used to study political communication, including political speeches, debates, and news coverage of political events. It can also be used to study political advertising and the impact of political communication on public opinion and voting behavior.
• Marketing Research: Content analysis can be used to study advertising messages, consumer reviews, and social media posts related to products or services. It can provide insights into consumer preferences, attitudes, and behaviors.
• Health Communication: Content analysis can be used to study health communication, including the representation of health issues in the media, the effectiveness of health campaigns, and the impact of health messages on behavior.
• Education Research : Content analysis can be used to study educational materials, including textbooks, curricula, and instructional materials. It can provide insights into the representation of different topics, perspectives, and values.
• Social Science Research: Content analysis can be used in a wide range of social science research, including studies of social media, online communities, and other forms of digital communication. It can also be used to study interviews, focus groups, and other qualitative data sources.

Examples of Content Analysis

Here are some examples of content analysis:

• Media Representation of Race and Gender: A content analysis could be conducted to examine the representation of different races and genders in popular media, such as movies, TV shows, and news coverage.
• Political Campaign Ads : A content analysis could be conducted to study political campaign ads and the themes and messages used by candidates.
• Social Media Posts: A content analysis could be conducted to study social media posts related to a particular topic, such as the COVID-19 pandemic, to examine the attitudes and beliefs of social media users.
• Instructional Materials: A content analysis could be conducted to study the representation of different topics and perspectives in educational materials, such as textbooks and curricula.
• Product Reviews: A content analysis could be conducted to study product reviews on e-commerce websites, such as Amazon, to identify common themes and issues mentioned by consumers.
• News Coverage of Health Issues: A content analysis could be conducted to study news coverage of health issues, such as vaccine hesitancy, to identify common themes and perspectives.
• Online Communities: A content analysis could be conducted to study online communities, such as discussion forums or social media groups, to understand the language, attitudes, and beliefs of the community members.

Purpose of Content Analysis

The purpose of content analysis is to systematically analyze and interpret the content of various forms of communication, such as written, oral, or visual, to identify patterns, themes, and meanings. Content analysis is used to study communication in a wide range of fields, including media studies, political science, psychology, education, sociology, and marketing research. The primary goals of content analysis include:

• Describing and summarizing communication: Content analysis can be used to describe and summarize the content of communication, such as the themes, topics, and messages conveyed in media content, political speeches, or social media posts.
• Identifying patterns and trends: Content analysis can be used to identify patterns and trends in communication, such as changes over time, differences between groups, or common themes or motifs.
• Exploring meanings and interpretations: Content analysis can be used to explore the meanings and interpretations of communication, such as the underlying values, beliefs, and assumptions that shape the content.
• Testing hypotheses and theories : Content analysis can be used to test hypotheses and theories about communication, such as the effects of media on attitudes and behaviors or the framing of political issues in the media.

When to use Content Analysis

Content analysis is a useful method when you want to analyze and interpret the content of various forms of communication, such as written, oral, or visual. Here are some specific situations where content analysis might be appropriate:

• When you want to study media content: Content analysis is commonly used in media studies to analyze the content of TV shows, movies, news coverage, and other forms of media.
• When you want to study political communication : Content analysis can be used to study political speeches, debates, news coverage, and advertising.
• When you want to study consumer attitudes and behaviors: Content analysis can be used to analyze product reviews, social media posts, and other forms of consumer feedback.
• When you want to study educational materials : Content analysis can be used to analyze textbooks, instructional materials, and curricula.
• When you want to study online communities: Content analysis can be used to analyze discussion forums, social media groups, and other forms of online communication.
• When you want to test hypotheses and theories : Content analysis can be used to test hypotheses and theories about communication, such as the framing of political issues in the media or the effects of media on attitudes and behaviors.

Characteristics of Content Analysis

Content analysis has several key characteristics that make it a useful research method. These include:

• Objectivity : Content analysis aims to be an objective method of research, meaning that the researcher does not introduce their own biases or interpretations into the analysis. This is achieved by using standardized and systematic coding procedures.
• Systematic: Content analysis involves the use of a systematic approach to analyze and interpret the content of communication. This involves defining the research question, selecting the sample of content to analyze, developing a coding scheme, and analyzing the data.
• Quantitative : Content analysis often involves counting and measuring the occurrence of specific themes or topics in the content, making it a quantitative research method. This allows for statistical analysis and generalization of findings.
• Contextual : Content analysis considers the context in which the communication takes place, such as the time period, the audience, and the purpose of the communication.
• Iterative : Content analysis is an iterative process, meaning that the researcher may refine the coding scheme and analysis as they analyze the data, to ensure that the findings are valid and reliable.
• Reliability and validity : Content analysis aims to be a reliable and valid method of research, meaning that the findings are consistent and accurate. This is achieved through inter-coder reliability tests and other measures to ensure the quality of the data and analysis.

Advantages of Content Analysis

There are several advantages to using content analysis as a research method, including:

• Objective and systematic : Content analysis aims to be an objective and systematic method of research, which reduces the likelihood of bias and subjectivity in the analysis.
• Large sample size: Content analysis allows for the analysis of a large sample of data, which increases the statistical power of the analysis and the generalizability of the findings.
• Non-intrusive: Content analysis does not require the researcher to interact with the participants or disrupt their natural behavior, making it a non-intrusive research method.
• Accessible data: Content analysis can be used to analyze a wide range of data types, including written, oral, and visual communication, making it accessible to researchers across different fields.
• Versatile : Content analysis can be used to study communication in a wide range of contexts and fields, including media studies, political science, psychology, education, sociology, and marketing research.
• Cost-effective: Content analysis is a cost-effective research method, as it does not require expensive equipment or participant incentives.

Limitations of Content Analysis

While content analysis has many advantages, there are also some limitations to consider, including:

• Limited contextual information: Content analysis is focused on the content of communication, which means that contextual information may be limited. This can make it difficult to fully understand the meaning behind the communication.
• Limited ability to capture nonverbal communication : Content analysis is limited to analyzing the content of communication that can be captured in written or recorded form. It may miss out on nonverbal communication, such as body language or tone of voice.
• Subjectivity in coding: While content analysis aims to be objective, there may be subjectivity in the coding process. Different coders may interpret the content differently, which can lead to inconsistent results.
• Limited ability to establish causality: Content analysis is a correlational research method, meaning that it cannot establish causality between variables. It can only identify associations between variables.
• Limited generalizability: Content analysis is limited to the data that is analyzed, which means that the findings may not be generalizable to other contexts or populations.
• Time-consuming: Content analysis can be a time-consuming research method, especially when analyzing a large sample of data. This can be a disadvantage for researchers who need to complete their research in a short amount of time.

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• Methodology

Content Analysis | A Step-by-Step Guide with Examples

Published on 5 May 2022 by Amy Luo . Revised on 5 December 2022.

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

• Books, newspapers, and magazines
• Speeches and interviews
• Web content and social media posts
• Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding). In both types, you categorise or ‘code’ words, themes, and concepts within the texts and then analyse the results.

Table of contents

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyse.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects, or concepts in a set of historical or contemporary texts.

In addition, content analysis can be used to make qualitative inferences by analysing the meaning and semantic relationship of words and concepts.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

• Finding correlations and patterns in how concepts are communicated
• Understanding the intentions of an individual, group, or institution
• Identifying propaganda and bias in communication
• Revealing differences in communication in different contexts
• Analysing the consequences of communication content, such as the flow of information or audience responses

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• Unobtrusive data collection

You can analyse communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

• Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

• Highly flexible

You can conduct content analysis at any time, in any location, and at low cost. All you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions.

• Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct  research question .

Next, you follow these five steps.

Step 1: Select the content you will analyse

Based on your research question, choose the texts that you will analyse. You need to decide:

• The medium (e.g., newspapers, speeches, or websites) and genre (e.g., opinion pieces, political campaign speeches, or marketing copy)
• The criteria for inclusion (e.g., newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
• The parameters in terms of date range, location, etc.

If there are only a small number of texts that meet your criteria, you might analyse all of them. If there is a large volume of texts, you can select a sample .

Step 2: Define the units and categories of analysis

Next, you need to determine the level at which you will analyse your chosen texts. This means defining:

• The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
• The set of categories that you will use for coding. Categories can be objective characteristics (e.g., aged 30–40, lawyer, parent) or more conceptual (e.g., trustworthy, corrupt, conservative, family-oriented).

Step 3: Develop a set of rules for coding

Coding involves organising the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

Step 4: Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti , and Diction , which can help speed up the process of counting and categorising words and phrases.

Step 5: Analyse the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context, and audience of the texts.

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Luo, A. (2022, December 05). Content Analysis | A Step-by-Step Guide with Examples. Scribbr. Retrieved 5 July 2024, from https://www.scribbr.co.uk/research-methods/content-analysis-explained/

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4 Qualitative Content Analysis

• Published: November 2015
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This chapter examines qualitative content analysis, a recent methodological innovation. Qualitative content analysis is defined and distinguished here from basic and interpretive approaches to content analysis. Qualitative content analysis is also distinguished from other qualitative research methods, though features and techniques overlap with other qualitative methods. Key differences in the predominant use of newly collected data and use of non-quantitative analysis techniques are detailed. Differences in epistemology and the role of researcher self-awareness and reflexivity are also discussed. Methods of graphic data presentation are illustrated. Three short exemplar studies using qualitative content analysis are described and examined. Qualitative content analysis is explored in detail in terms of its characteristic components: (1) the research purposes of content analysis, (2) target audiences, (3) epistemological issues, (4) ethical issues, (5) research designs, (6) sampling issues and methods, (7) collecting data, (8) coding and categorization methods, (9) data analysis methods, and (10) the role of researcher reflection.

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Three approaches to qualitative content analysis

Affiliation.

• 1 Fooyin University, Kaohsiung Hsien, Taiwan.
• PMID: 16204405
• DOI: 10.1177/1049732305276687

Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.

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ORIGINAL RESEARCH

Qualitative content and discourse analysis comparing the current consent systems for deceased organ donation in spain and england.

• 1 NHS England, London, United Kingdom
• 2 School of Medical and Health Sciences, Bangor University, Bangor, United Kingdom
• 3 Donation and Transplant Coordination Section, Hospital Clinic, Barcelona, Spain
• 4 Surgical Department, University of Barcelona, Barcelona, Spain
• 5 Donation and Transplantation Institute Foundation, Barcelona, Spain
• 6 NHS Blood and Transplant, Watford, United Kingdom
• 7 Policy Innovation and Evaluation Research Unit, London School of Hygiene and Tropical Medicine, London, United Kingdom

England switched to an opt-out system of consent in 2020 aiming to increase the number of organs available. Spain also operates an opt-out system yet has almost twice the organ donations per million population compared with England. We aimed to identify both differences and similarities in the consent policies, documents and procedures in deceased donation between the two countries using comparative qualitative content and discourse analysis. Spain had simpler, locally tailored documents, the time taken for families to review and process information may be shorter, there were more pathways leading to organ donation in Spain, and more robust legal protections for the decisions individuals made in life. The language in the Spanish documents was one of support and reassurance. Documents in England by comparison appeared confusing, since additions were designed to protect the NHS against risk and made to previous document versions to reflect the law change rather than being entirely recast. If England’s ambition is to achieve consent rates similar to Spain this analysis has highlighted opportunities that could strengthen the English system-by giving individuals’ decisions recorded on the organ donor register legal weight, alongside unifying and simplifying consent policies and procedures to support families and healthcare professionals.

Introduction

Since England switched to an opt-out consent system in May 2020, with the aim of making more organs available for transplant through the introduction of deemed consent, consent rates for deceased organ donation have not increased [ 1 ]. Despite high ambitions [ 2 ] England still appears to be falling short of the number of organs available achieved by Spain which has consistently had much higher organ donation rates (46.7 per million population in 2022) despite having a similar legal framework for deceased organ donation consent [ 3 ].

Presumed consent (sometimes referred to as deemed consent) means that a person is considered to have no objection to donating their organs after death unless they have registered or informed someone close to them that they do not wish to do so. There have been many studies that have concluded that presumed consent alone does not explain the fluctuation in donation rates between countries [ 4 ]. Legislation, public knowledge and awareness of organ donation, donor availability and characteristics, religious beliefs, transplant service infrastructure and healthcare system capacity (e.g., in intensive care), all play a part in making more organs available for transplant, but their relative importance is unclear [ 5 – 8 ].

England implemented their opt-out system to increase the consent rate for deceased organ donation, the assumption being that more organs would become available for transplant. However the opt-out legislation was nested within the existing opt-in system, and despite the addition of the new deemed consent pathway, the failure to secure consent for deceased organ donation retrieval from those involved in end-of-life discussions is still widely regarded as the single most important obstacle to making more organs available for transplant in England [ 9 ].

The purpose of this analysis was to identify differences and similarities in consent policies and associated documents between England and Spain and to consider whether there are opportunities to further increase consent rates for deceased organ donation and improve current practice in England.

Overall Context and Scope

This analysis was undertaken as part of a broader evaluation of the impact of opt-out in England on the organ donation system [ 10 ]. During the study it became clear that the processes involved in consent, in particular were lengthy, excessive and negatively impacted families in England [ 11 ]. Specialist staff involved in consent also felt the process to be excessive and burdensome [ 12 ]. The research team was aware of extensive research into gold standards in terms of pathways to organ donation, i.e., what should happen and by whom to achieve the desired outcomes (the so-called Spanish model) [ 13 ] but was unable to find examples (or research) of the consent documents used in practice in countries with opt-out systems, which are considered leaders in organ donation. We felt that as the main study was specifically commissioned to examine a policy that (in theory) shifts from a model of informed consent to a model of presumed consent it would be a worthwhile and interesting analysis to look more closely at the consent documents and associated policies and guidelines of the world’s leading country with an opt-out system and compare them.

Materials and Methods

Research question.

What are the differences in roles, processes, consent forms and practices between the Spanish and English systems of organ donation and how do any identified differences begin to explain the higher consent rates in Spain?

Data Collection

We identified and obtained key policy and procedure documents and consent forms from the websites of the “Organizacion Nacional de Transplantes” (ONT) in Spain and NHS Blood and Transplant (NHSBT) in England. Documents published in Spanish were translated into English for analysis using the “TransPerfect” computer software. Table 1 lists the documents included in the analysis [ 14 – 22 ]. The documents were read, reread, compared and coded.

Table 1 . Documents included in the analysis.

We worked with a Spanish intensive care doctor (co-author) via email and two online team sessions to clarify the correct interpretation of the documents and the donation system. This enabled us to verify that the current practice was in line with the written protocols. We engaged stakeholders through meetings with academics and cliniciansorganized by the European Society for Organ Transplantation (ESOT) to help establish the context of the English and Spanish organ donation systems within which the documents for analysis were produced. We consulted with a United Kingdom senior nurse (co-author) involved in the English NHSBT education program and United Kingdom legislation. A summary flowchart of the Spanish and English organ donation structures and processes was made for comparison ( Figures 1 , 2 ).

Figure 1 . Flow chart of the English and Spanish process constructed from documents (20–22) and stakeholder engagement.

Figure 2 . Flow chart of the Spanish process constructed from documents (14–19).

These processes helped to build a better understanding of broad cultural factors, such as religious beliefs, ethnic diversity, family dynamics, the reaction of families to the system and whether they had ever challenged the law, and how these might be underpinning any differences observed in the documents analyzed in detail.

Data Analysis

Qualitative content analysis was used to code, analyze, compare and interpret the textual data and diagrams in the included documents to gain insight into the meaning and context of the policy, and the links between content, process and outcome [ 23 ].

Coding involved assigning attributes to words, sentences, or paragraphs to compare and contrast content, process and meaning. Consent forms were compared for structure, content, and length [ 24 ].

Principles of critical discourse analysis were used to make additional interpretations of the text, complemented by engagement with experts in the Spanish and English systems. This was done to systematically explore the often-opaque relationships between what is written (i.e., policies, guidelines) and what happens in practice, with multiple stakeholders, many with different objectives. This process helped to examine, for example, who or what the subjects and objects are in the respective structures, discourses and processes, and how and why the two systems manage to generate and maintain different forms of language (rhetoric) [ 25 ]. The flowcharts constructed ( Figures 1 , 2 ) helped to show where objects in relation to consent such as the Organ Donor Register, the roles of the staff, e.g., clinicians, transplant coordinators, nursing staff and the role and hierarchy of the family, etc. fit together in a complex system. The rhetorical analysis specifically searched for opportunities to give or decline consent within the process. This enabled us to understand more about the mechanisms underpinning the Spanish consent pathway, and thus extrapolate findings that may be applicable, with adaptation, to England [ 26 ].

Author Reflexivity

Co-authors LM and JN were already working with colleagues in the English system and were connected via multiple professional networks to clinical and academic colleagues in Spain. Once the lead investigators had a good understanding of the two systems, we had further discussions with the Spanish consultant and Senior United Kingdom nurse co-authors to validate the interpretation of the two systems. We presented this work at several multi-disciplinary meetings and events including the Deconstructing Donation Special Interest Group for additional critique and insight. We adapted the recommendations for rigor, transparency, evidence, and representation to present the results [ 27 ].

Box 1 provides a comparison of some key performance indicators in England and Spain in the year 2022.

Box 1 Comparison of key performance indicators between Spain and England 2022.

A direct comparison of the systems, processes, and cultural and linguistic styles between Spain and England in relation to consent for deceased organ donation is described below. Table 2 highlights similarities and differences within the systems with specific reference to consent ( Table 2 ). The mechanisms that may or may not be a factor in achieving the desired outcomes in relation to consent are further unpacked and described in Table 3 .

Table 2 . Similarities and differences within the Spain and England systems with specific reference to consent.

Table 3 . Mechanisms which may be a factor in bringing about the desired outcomes, or not, in relation to consent.

Overall System

England has a diverse population with deep-rooted Christian traditions and multi-faith communities. England switched to an opt-out system of consent to deceased organ donation in May 2020. The organ donation system is run by the NHSBT, which is publicly funded and not privately available. Deceased organ donation is considered for those who die from brain stem or controlled circulatory death. Donation is therefore only possible for those who are admitted to an intensive care unit (ICU), but ICU admission is for treatment and prognostic purposes, not for organ donation [ 28 – 30 ].

England has an intensive care bed capacity of approximately 6.6 per 100,000 people [ 31 ]. Organ donation is possible in every acute NHS hospital. When the patient is identified as a potential donor the clinical team caring for the patient will refer the patient via a national referral number. The regional NHSBT team will assess the patient and mobilize a Specialist Requester (SR) or Specialist Nurse in Organ Donation (SNOD) – depending on who is available. After checking the national Organ Donor Register (ODR), the SNOD/SR will visit the unit and approach the family about donation – this is a nurse-led process and care pathway. The ODR has various options (e.g., opt-in, opt-out, nominate a representative and the ability to specify a small number of organs/tissues that people do or do not want to donate after death) but it has no legal status and family members have the ability to override it in practice and even register a decision on behalf of their loved one. Hospitals are reimbursed a small sum for facilitating organ donation, (approximately 1,000 pounds per donor) but this figure has not increased substantially over time and complex and bureaucratic finance systems often make it difficult to spend and save money to promote organ donation.

Spain is a predominantly Catholic country and has had an organ donation system for 44 years [ 32 ]. The organ donation system is overseen by the ONT. It is possible to be an organ donor while being treated privately, by being transferred to the public health system for donation purposes only. In addition to the pathways in England, deceased organ donation can be obtained from sudden unexpected circulatory deaths and those undergoing euthanasia. Spain has an intensive care bed capacity of approximately 9.7 beds per 100,000 people [ 33 ]. In Spain, patients admitted to the Emergency Department with catastrophic brain or cardiac damage where treatment is considered futile, can be intubated, and admitted to the ICU for the purpose of organ donation [ 34 ]. Also, those who are suspected of developing brain death or have already been declared brain dead in private institutions or the Emergency Department, can be admitted to the ICU solely for the purpose of organ donation, unlike in England.

Spain has dedicated hospitals where deceased organ retrieval can occur, with designated transplant coordinators (TC) in each of these hospitals (approximately 70% being physicians and 30% nurses). Often in hospitals with no TC, there will be proactive ICU staff who can identify donors. They can request support from a dedicated hospital which will usually send a TC to aid in speaking with the family. Any healthcare professional can contact the TC regarding a potential donor. Once alerted to a potential donor the TC will visit the potential case, review the medical records, and determine whether or not there is a “ prior instructions document. ” This document has legal status.

System Processes Concerning Deceased Organ Donation Consent

In England “ the individual leading the family approach for organ donation must be suitably trained and qualified with sufficient knowledge and skills to sensitively answer any questions and have the time to support the family,” [21, pg 9]. In practice, this is always the SNOD/SR, anybody outside of this role is actively discouraged from discussing organ donation [ 12 ].

As illustrated in Figure 1 , the English system has many pathways to consent. If the deceased opted for organ donation during their lifetime, this is discussed with the family to ensure that this was the last known decision. If the deceased had opted out of the ODR “ providing work load allows, the SNOD should also discuss with the family if this was the last known decision.” [SIC] [20, pg 11]. If this is not possible due to workload, the SNOD/SR will “ coach the clinician in the discussion to have with the family and agree actions.” If the clinician feels unable to do this, the family will have to wait for the arrival of the SNOD/SR. In practice, detailed discussions with the family when the deceased has opted out rarely happen due to limited resources and concerns about NHSBT being seen as pushing for organ donation when the deceased has opted out.

Another pathway, although rare, is the “nominated representative,” where a person nominates someone else to make a decision on their behalf before they die. “ If despite all reasonable efforts the nominated representative cannot be contacted in time or to make a decision, then consent may be deemed. ” [SIC] [21, pg 19] Nonetheless, the donation can only take place after the family has also been consulted.

Only after the SNOD/SR has established that none of the above pathways apply, can they check whether consent can be assumed. If the family cannot agree, despite being given time and further information, then “ the hierarchy of consent, i.e., highest qualifying relationship,” applies but the final decision to proceed lies with the [SNOD/SR]. ’ [SIC] In reality, it is the family member with the strongest voice (either for or against donation) whose wishes are followed [ 11 , 35 ]. In addition, the SNOD/SR cannot proceed with the donation unless they have the full support (and permission) of the treating clinical team(s). If the family cannot be contacted and there is no prior expression of a decision, then although “consent could be deemed it is advised that donation must not proceed.’ ”[SIC] [21, pg 17].

To override a decision, families need only provide a “ level of information that would lead a reasonable person to conclude that they [i.e., the deceased] did not want to be a donor.” [21, pg 24]. This may be verbal or written. Any evidence from any family member at this point can be taken into account. [21, pg 18] The SNOD/SR will make a judgment about the reliability of the information and whether it is right for the donation to proceed. “ Sometimes clinical staff will reach the judgement that although there is a legal basis to proceed with the donation, the human considerations involved mean that it should not go ahead. While the presence of appropriate consent permits organ and tissue donation to take place, it does not mandate that it must….(and) where the risks to public confidence might outweigh the benefits of donation proceeding, donation should not proceed even though the law permits it. ” [SIC] [21, pg 7].

In Spain, there is no organ donor register but a prior instructions document is available from the patient’s GP. Patients can register their consent or refusal to be an organ donor in the document which will be made available in the local Advance Directives Registry. Their families will be approached and informed of the recorded decision. If a “No” to the donation has been recorded, the family will still be asked if there has been any recent change to this decision. However, there would have to be substantial evidence to overturn this notion since the prior instructions document has legal value and is signed by a witness.

It is recommended that the healthcare professional who mentions organ donation be different from the professional who has discussed the likelihood of the patient dying to avoid a conflict of interest for the TC who may also have a role as an intensivist, etc. It is mandatory in some hospitals that the TC be contacted before withdrawal of treatment in the ICU, a condition introduced by some hospital medical directors.

The Consent Forms

The English consent form is seven pages long, with all organs, tissues and retrieval processes listed as yes/no checkboxes, including options for additional information. The family will need to answer “Yes” or “No” to everything irrespective of what the deceased had registered about what organs they wanted to donate while they were alive and this will include organ donation for research (not just therapeutic purposes). The family will be made aware that the decision can be revoked until “knife to skin.” [ 20, pg 24]. The family members “ are encouraged to sign the consent form ” although there is no legal obligation to do so. The process may take hours to days. The SNOD/SR will document the conversation in the patient’s notes and on the NHSBT’s national digital system, also verified by a witness. If the family were to override the decision or revoke consent this will be respected and the reasons would be acknowledged and recorded by the SNOD/SR.

Each Spanish region has its own form based on examples from the ONT protocols. Often they are a single page requesting the name and relationship of the relative and the date. Some do have a free text space for the family to write what organs or tissues they believe the deceased would not have wished to donate. In other cases, these wishes are documented in the medical notes instead. Once a decision is reached after discussion with the family, it is mandatory that the consent form be signed by the dissenting family member(s).

Approach and Language for Consent

In England, when families are approached, they are asked, “ what the potential donor’s last decision would have been and whether the deceased expressed any thoughts on becoming a donor” [SIC]. The guidelines suggest that SNODs/SRs should establish who is the next of kin (in accordance with the established highest qualifying relationship guidance) and approach that relative to organ donation. Although the opportunity to help others is often mentioned, the overall guidelines suggest that the SNOD/SR should remain impartial [ 21 ].

In Spain, if there is no recorded decision made while alive, the family is generally asked: “ what would have been the willingness of the deceased to donate their organs to help other people?” [SIC] [ 16, pg 197]. “If the family are in doubt, the TC can assist in decision-making, reinforcing positive verbalisations to donation and courage in those moments, and conveying ideas of generosity and proximity and enquiring whether the deceased gave to charity or donated blood during their lifetime, etc.” [SIC] [16, pg 126].

In the case of large families, the TC seeks to speak to the “key family” member. The key family member is identified through discussion with the family and the knowledge of the staff caring for the patient. Should a family be divided over the issue of donation, the TC will not proceed. If there is no family present, the TC “strive(s) through links with social services and the police to find a family member”[16, pg 120] but may still consider organ donation if no family can be found.

Should the family decline the donation, “ it is important to make it clear that the decision is respected and understood but that, however, it is advisable to think about the matter more slowly without the presence of a TC.”[16, pg 126] The TC also explores the reasoning behind the refusal and corrects misunderstandings. The TC may approach the family as often as necessary.

During the consent process, the family is usually asked which organs they believe the deceased would not want to donate. The conversation aims to combine “ speed and effectiveness in communicating with families, with respect for ethical principles and transparency that must preside over the process .” [SIC] [16, pg 116] On average, the process of gaining consent takes 30 min.

This is the first detailed documentary comparison between the Spanish and English opt-out systems of consent to organ donation. The biggest differences observed were that the Spanish system was less complex in terms of consent, evidently pro-donation with a willingness to take some risks, likely to take less time, better resourced, with better access to ICU beds and a more locally tailored opt-out system with some legal protection for the potential organ donor’s life choices. England in contrast has a more complex centralized system with risk-adverse protocols, an itemized approach to consent, implemented in a country where there are fewer ICU beds, and no legal protection for the potential organ donor.

The Spanish system covers both public and private hospitals and has dedicated resources for organ donation, such as stand-alone centers and in-hospital beds. In England, for deceased organ donation, the NHSBT only covers NHS hospitals so some potential donors in the private sector are lost. There are no dedicated resources in England organ donation takes place when the system has the capacity to manage it which can potentially lead to frustration and disengagement of non-specialist staff. Euthanasia and organ donation are legal in Spain (illegal in England) and although the pathway is relatively recent it has created an additional platform to embed organ donation as a routine end-of-life process–the initial requests for this pathway have come from people who had requested euthanasia and not in the originaleuthanasia protocols. Potential organ donors with neurodegenerative conditions requesting euthanasia also tend to be younger without underlying co-morbidities and a single donor could potentially decide to donate all of their organs and tissues to help others, again increasing the visibility of organ donation in the system.

Families are as involved in decision-making in Spain as they are in England, but the consent process is shorter in Spain. The language used with family members and staff was also observed to be different in tone and meaning. The English system focuses on establishing the “ last known decision of the deceased ”whereas the Spanish system aims to establish “ the will of the potential organ donor to donate their organs as well as the will to help others.” In England, current guidelines and codes of conduct reflect the human tissue authority’s position on consent to organ and tissue retrieval. This appears to be more in line with the old “opt-in” system and thus encourages unnecessary risk aversion which is contrary to the spirit of the opt-out legislation and appears confusing and neutral.

Organ donation appears to be more embedded within the Spanish healthcare system as an integral part of end-of-life care, with many healthcare professionals being aware of it and being encouraged to be involved with it. As such, it may be more likely to be discussed by families as there may be a healthcare worker in the family or someone they know who has been through the process before.

The legally binding prior instructions document is also available from the GP or local hospital and is signed with a witness present. Therefore, the witness, i.e., an accompanying family member is likely to be able to verify the document. Once completed, it is part of the person’s local medical record, meaning that there is a more complex process if family members want to challenge their loved one’s organ donation decision in life. There is a significant risk to donor decisions in England as anybody can go onto the ODR and register - SNODs/SRs continue to find cases where the opt-out decision was registered at a time when the person was being ventilated in the ICU [ 9 ].

The structure of the hospitals - i.e., that specific hospitals manage deceased organ donation, that patients can be admitted to the ICU purely for the need to ventilate organs and drug infusion in preparation for donation - is also very different from England. Matching the Spanish approach would undoubtedly cost the NHS more at the expense of another area of the health service. However, Spain states that “ the social value of organ donation justifies staff efforts and the economic cost involved” [SIC] [16, pg 195], indicating an overall difference in priority in terms of deceased organ donation between the two countries.

In addition to the marked differences in the provision of ICU beds required for organ donation to proceed, in 2019, Spain had 3 hospital beds per 1,000 people whereas England had 2.5 beds per 1,000 people. In 2019, Spain had a bed occupancy rate of 76%, whereas in England the same rate was 92% for general and acute overnight beds [ 33 ]. Given the relentless pressure on NHS staff to continuously manage such a high bed occupancy rate, it becomes clearer why a centralized system of organ donation was implemented via a separate NHS body (NHSBT) with its own governance and management structures [ 36 ]. The NHSBT was created in 2008 and to a certain extent, its centralized opt-in system was successful in that consent rates steadily increased over the following decade before the law was changed. Nonetheless, the NHSBT has not been able to replicate the success of Spain. In 2020, a “soft” opt-out was implemented within the existing centralized national system alongside the existing opt-in system, and the two systems have been operating together in a complex way ever since. Although Spain does also offer the ability for patients to opt-in through their decision on the prior instructions document, this is rarely seen since the Spanish public trusts the organ donation system and knows that their families will always be consulted so they do not see it as important to record their life decisions. This makes the law appear more consistent and in line with a system of presumed consent, unlike in England [ 37 ].

Recommendations for Policy and Practice in England and the United Kingdom

The NHS is built on the ethos that “if it is not written down it did not happen.” This has been generally applied to mitigate any potential legal action against staff or the NHS in the future. This is partially why consent documents and protocols tend to reflect ambiguity and risk aversion when compared with Spain which appears more comfortable with the spirit of presumed consent. However this is potentially creating a context where SNODs/SRs are not able to openly and proactively emphasize the benefits of organ donation or feel fully supported to do so with families. We suggest, given a change in legislation that has changed the default for nearly 60 million citizens to support the donation of their organs after they die, unless they say otherwise, that documents and standard operating procedures, particularly in relation to consent, reflect this and are revised with a view to simplification and presumption.

The ODR also lacks legal status. Approximately 10% of families override their relatives’ opt-in decision but the same rates are not observed for opt-out decisions. Despite having an ODR, it is not mandatory to follow the organ donation decision on the register. If the ODR was given greater legal status and the decisions in it were used as a basis for the conversation with the family after death (preferably by simplifying the latter to bring it more in line with the Spanish approach to consent after death), this could make it easier for the family to support the potential donor’s decision. It may also create a context in which people are more likely to discuss what they want in terms of organ donation. Aligning language, processes and guidelines with the legislation on presumed consent may generate a more positive initial response to organ donation and help address doubts or concerns that are common in these complex end-of-life discussions.

The linking of the ODR to a patient’s medical record can also make it easier for healthcare professionals to discuss with the patient if they still stand by their recorded decision should anything life-threatening happen during their admission, similar to a “Do Not Attempt Resuscitation” form.

Although organ donation has expanded in Spain, the underlying principles of legal standing, guidelines and protocols have not changed substantially. Since 2021 the latest NHSBT consent manual has had six updates. The most recent updates are included in Figure 3 for reference. The consent form has undergone multiple revisions in recent years, with each iteration adding further layers of complexity and processes. This is wasteful and prevents opportunities for innovation that would benefit patients at an individual level. Any revisions to the documents need to be more mindful of the users (e.g., SNODs/SRs and acutely bereaved families) and provide a more personalized and sensitive approach to consent that is aligned with the ambitions of opt-out legislation.

Figure 3 . Latest update to the consent manual in England.

Limitations

Due to resource constraints, we were not able to back-translate very long policy documents from English into Spanish. We relied on software to translate Spanish documents and then verified key concepts and processes with a small number of Spanish experts.

Policy documents alone do not entirely reflect actual practice and there is of course variation in the implementation of processes within each health system. We acknowledge this limitation and mitigated it by engaging with organ donation practitioners in Spain and England as co-authors to complement our documentary analysis with their perceptions, experiences and knowledge. There are also significant differences within and between countries that are not reflected in a discourse analysis focused on consent such as detailed public attitudes to organ donation [ 37 , 38 ] as well as potential donor characteristics and methods of optimizing organ donor potential which vary widely.

Finally England has a much higher number of live donors than Spain, emphasizing the complexity of organ donation and the fact that there are more ways to increase the number of organs available, reflecting that deceased donor consent rates are not the only measure of a successful organ donation system.

The Spanish system has a simpler and more streamlined approach to family consent to organ donation and the documents very proactively encourage donation. If England’s ambition is to achieve the consent rates consistently seen in Spain, there may be opportunities to do so by giving greater legal protection and status to the ODR and also by changing the culture from being impartial and risk-averse toward the promotion of organ donation. Significant investment in staff and resources would also be required to match the availability of ICU beds seen in Spain as well as dedicated resources, including specialist sites, which were previously deemed too expensive to invest in. However, there are potentially modifiable issues that appear to work better in Spain such as a shorter and simpler consent process and much more positive language throughout the process, which would improve the experience of staff and acutely bereaved families. In parallel, research is needed (ideally in a controlled context [ 39 ]) to understand more about what works, for whom and why in order to maintain the supply of organs to meet the increasing demand.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Ethics Statement

The main study received favorable ethics opinions from the LSHTM Research Ethics Committee (Ref. 26427 – 20/07/2021) and from the Health Research Authority (HRA) Research Ethics Committee (Ref. 21/NW/0151 – 03/06/2021). Research approvals were received from the HRA and Health and Care Research (HCRW) Wales (IRAS project ID 297313) on 3/06/2021, and from the Research, Innovation and Novel Technologies Advisory Group (RINTAG) (ODT Study no 113; NHSBT Change Control ref: cc/11164) on 11/06/2021. This particular analysis involved comparing publicly accessible protocol and policy documents and engagement with experts by opinion, specific ethical approval for this work was not required. At the time of publication, a revised 2-page consent form has received approval from the HTA and educational research is in progress (Miller) following the implementation of opt-out.

Author Contributions

KR - undertook first draft of protocol, translation and analysis of documents, prepared the initial report and contributed to preparing the manuscript for submission. JN – conceptualised the study, reviewed the protocol, contributed to primary analysis, reviewing the draft and preparing the manuscript for submission. LM – conceptualised the study, reviewed the protocol, undertook stakeholder engagement, contributed to primary analysis, reviewing the draft and preparing the manuscript for submission. NM -Reviewed the protocol and contributed to preparing the manuscript for submission. DP-Z – contributed to data analysis, stakeholder engagement and reviewed the manuscript for submission. CM – contributed to data analysis, stakeholder engagement, contextual understanding and reviewed the manuscript for submission. All authors contributed to the article and approved the submitted version.

The author(s) declare(s) that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the NIHR Policy Research Programme through its core support to the Policy Innovation and Evaluation Research Unit (Project No: PR-PRU-1217-20602). The views expressed are those of the author(s) and are not necessarily those of the NIHR or the Department of Health and Social Care.

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.

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Keywords: organ donation, consent, England, Spain, opt-out

Citation: Rees K, Mclaughlin L, Paredes-Zapata D, Miller C, Mays N and Noyes J (2024) Qualitative Content and Discourse Analysis Comparing the Current Consent Systems for Deceased Organ Donation in Spain and England. Transpl Int 37:12533. doi: 10.3389/ti.2024.12533

Received: 06 December 2023; Accepted: 10 June 2024; Published: 04 July 2024.

Copyright © 2024 Rees, Mclaughlin, Paredes-Zapata, Miller, Mays and Noyes. 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: Leah Mclaughlin, [email protected] ; Jane Noyes, [email protected]

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Acknowledgements

Findings described in this manuscript were presented at the annual meetings for the Midlife in the United States Study (May 2023 in Madison, WI) and the American Psychosomatic Society (March 2023 in San Juan, Puerto Rico). Findings were also presented virtually at the core meeting for the Network for Emotional Well-Being (April 2023). This work was supported by a grant from the Network for Emotional Well-being and Stress Measurement Network (through the National Institutes of Health). The MIDUS Study was supported by the National Institute on Aging at the National Institutes of Health (P01-AG020166; U19-AG051426) to conduct a longitudinal follow-up of the MIDUS investigation. The baseline MIDUS study was supported by the John D. and Catherine T. MacArthur Foundation Research Network on Successful Midlife Development. Support also came from the following grants M01- RR023942 (Georgetown), M01-RR00865 (UCLA) from the General Clinical Research Centers Program and 1UL1RR025011 (UW) from the Clinical and Translational Science Award (CTSA) program of the National Center for Research Resources, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. De-identified data and documentation for the MIDUS study are available at https://www.icpsr.umich.edu/web/ICPSR/series/203 . Analytic code and supporting methodological information are available at https://osf.io/jqsmh/ .

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Jennifer Morozink Boylan and Julia K. Boehm contributed equally to this work.

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Department of Health and Behavioral Sciences, University of Colorado Denver, Denver, CO, USA

Jennifer Morozink Boylan & Monica Adams

Department of Psychology, Chapman University, Orange, CA, USA

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Boylan, J.M., Adams, M. & Boehm, J.K. Social Structural Differences in Qualitative Perspectives on Well-Being. Applied Research Quality Life (2024). https://doi.org/10.1007/s11482-024-10344-7

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“It’s his cheerfulness that gives me hope”: A Qualitative Analysis of Access to Pediatric Cancer Care in Northern Tanzania

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Background Pediatric cancer is a significant and growing burden in low- and middle-income countries. The objective of this project was to describe the factors influencing access to pediatric cancer care in Northern Tanzania using the Three Delays Model.

Methods This was a cross-sectional qualitative study conducted between June and September 2023 at Kilimanjaro Christian Medical Centre (KCMC). Parents and caregivers of children obtaining pediatric cancer care at KCMC were approached for participation in in-depth interviews (IDIs) and a demographic survey. All IDIs were facilitated in Swahili by a bilingual research coordinator. Thirteen IDIs and surveys were completed during the study period. Analysis utilized inductive and deductive coding approaches to identify dominant themes and sub-themes impacting access to pediatric oncology care.

Results Participants reported significant financial barriers to accessing pediatric cancer care along the entire care continuum. Early delays were impacted by waiting for symptoms to resolve or worsen. The most substantial delays resulted from health infrastructure at mid-level health facilities, misdiagnoses, and delayed referral to KCMC for treatment. Participants did not describe delays after arrival to KCMC and rather offered perspective on their child’s cancer diagnosis, their concerns while obtaining care, and their hopes for the future. Financial support provided by the Tanzanian government was the only facilitator noted by participants.

Conclusions We suggest targeted interventions including 1) empowerment of CHWs and local traditional healers to advocate for earlier care seeking behavior, 2) implementation of clinical structures and training at intermediary medical centers aimed at earlier referral to a treatment facility, 3) incorporation of support and education initiatives for families of children with a cancer diagnosis. Lastly, we suggest that national health plans include pediatric cancer care.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This project was funded by the Duke Global Health Institute Graduate Student funds (MM and PE), and the National Institutes of Health K01 Grant #5K01TW012181 (ERS). No other authors received specific funding for this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Data storage and study procedures were approved by the Duke University and KCMC institutional review boards (IRBs) and the Tanzania National Institute for Medical Research (NIMR) prior to the commencement of the study. We utilized the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines which is included as supplemental information with this manuscript.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

Data are only available upon reasonable request and data transfer requires a written agreement approved by Kilimanjaro Christian Medical Centre Ethics Committee and the National Institute for Medical Research (Tanzania). Data inquiries can be sent to Gwamaka W. Nselela at gwamakawilliam14{at}gmail.com . A Data Transfer Agreement must be completed between the study investigators and the individual or entity requesting access to the study data before the data may be shared.

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