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Germ Defence - Machine Assisted Topic Analysis
No full textAnonymised dataset (please email corresponding author for decryption key) and open R STM analysis.</span
Applying machine-learning to rapidly analyse large qualitative text datasets to inform the COVID-19 pandemic response
No full textBackground: machine-assisted topic analysis (MATA) uses artificial intelligence methods to assist qualitative researchers to analyse large amounts of textual data. This could allow qualitative researchers to inform and update public health interventions ‘in real-time’, to ensure they remain acceptable and effective during rapidly changing contexts (such as a pandemic). In this novel study we aimed to understand the potential for such approaches to support intervention implementation, by directly comparing MATA and ‘human-only’ thematic analysis techniques when applied to the same dataset (1472 free-text responses from users of the COVID-19 infection control intervention ‘Germ Defence’). Methods: in MATA, the analysis process included an unsupervised topic modelling approach to identify latent topics in the text. The human research team then described the topics and identified broad themes. In human-only codebook analysis, an initial codebook was developed by an experienced qualitative researcher and applied to the dataset by a well-trained research team, who met regularly to critique and refine the codes. To understand similarities and difference, formal triangulation using a ‘convergence coding matrix’ compared the findings from both methods, categorising them as ‘agreement’, ‘complementary’, ‘dissonant’, or ‘silent’. Results: human analysis took much longer (147.5 hours) than MATA (40 hours). Both human-only and MATA identified key themes about what users found helpful and unhelpful (e.g. Boosting confidence in how to perform the behaviours vs Lack of personally relevant content ). Formal triangulation of the codes created showed high similarity between the findings. All codes developed from the MATA were classified as in agreement or complementary to the human themes. Where the findings were classified as complementary, this was typically due to slightly differing interpretations or nuance present in the human-only analysis. Conclusions: overall, the quality of MATA was as high as the human-only thematic analysis, with substantial time savings. For simple analyses that do not require an in-depth or subtle understanding of the data, MATA is a useful tool that can support qualitative researchers to interpret and analyse large datasets quickly. These findings have practical implications for intervention development and implementation, such as enabling rapid optimisation during public health emergencies. Contributions to the literature Natural language processing (NLP) techniques have been applied within health research due to the need to rapidly analyse large samples of qualitative data. However, the extent to which these techniques lead to results comparable to human coding requires further assessment. We demonstrate that combining NLP with human analysis to analyse free-text data can be a trustworthy and efficient method to use on large quantities of qualitative data. This method has the potential to play an important role in contexts where rapid descriptive or exploratory analysis of very large datasets is required, such as during a public health emergency.AcknowledgementsWe would like to thank our voluntary research assistants; Benjamin Gruneberg, Lillian Brady, Georgia Farrance, Lucy Sellors, Kinga Olexa, and Zeena Abdelrazig for their valuable contribution to the coding of the data for the human-only analysis. We would also like to acknowledge Katherine Morton’s contribution to the administration of survey, and James Denison-Day for the construction and maintenance of the Germ Defence website.Publication references - 26Show allSorted by: DateDeveloping and testing an automated qualitative assistant (AQUA) to support qualitative analysisRobert P Lennon, Robbie Fraleigh, Lauren J Van Scoy, Aparna Keshaviah, Xindi C Hu, Bethany L Snyder, Erin L Miller, William A Calo, Aleksandra E Zgierska, Christopher Griffin2021, Family Medicine and Community Health - Article22 total citations on Dimensions.Article has an altmetric score of 4View PDFAdd to LibraryAccelerating Mixed Methods Research With Natural Language Processing of Big Text DataTammy Chang, Melissa DeJonckheere, V. G. Vinod Vydiswaran, Jiazhao Li, Lorraine R. Buis, Timothy C. Guetterman2021, Journal of Mixed Methods Research - Article88 total citations on Dimensions.Article has an altmetric score of 12Add to LibraryAdapting Behavioral Interventions for a Changing Public Health Context: A Worked Example of Implementing a Digital Intervention During a Global Pandemic Using Rapid Optimisation MethodsKatherine Morton, Ben Ainsworth, Sascha Miller, Cathy Rice, Jennifer Bostock, James Denison-Day, Lauren Towler, Julia Groot, Michael Moore, Merlin Willcox, Tim Chadborn, Richard Amlot, Natalie Gold, Paul Little, Lucy Yardley2021, Frontiers in Public Health - Article1111 total citations on Dimensions.Article has an altmetric score of 5View PDFAdd to LibraryInfection Control Behavior at Home During the COVID-19 Pandemic: Observational Study of a Web-Based Behavioral Intervention (Germ Defence)Ben Ainsworth, Sascha Miller, James Denison-Day, Beth Stuart, Julia Groot, Cathy Rice, Jennifer Bostock, Xiao-Yang Hu, Katherine Morton, Lauren Towler, Michael Moore, Merlin Willcox, Tim Chadborn, Natalie Gold, Richard Amlôt, Paul Little, Lucy Yardley2021, Journal of Medical Internet Research - Article1010 total citations on Dimensions.Article has an altmetric score of 61View PDFAdd to LibraryCarrying Out Rapid Qualitative Research During a Pandemic: Emerging Lessons From COVID-19Cecilia Vindrola-Padros, Georgia Chisnall, Silvie Cooper, Anna Dowrick, Nehla Djellouli, Sophie Mulcahy Symmons, Sam Martin, Georgina Singleton, Samantha Vanderslott, Norha Vera, Ginger A. Johnson2020, Qualitative Health Research - Article197197 total citations on Dimensions.Article has an altmetric score of 60View PDFAdd to Library© 2022 Digital Science & Research Solutions, Inc. All Rights Reserved | About Dimensions · Privacy policy ·· Legal terms · VPAT ®<br/
Using machine‐assisted topic analysis to expedite thematic analysis of free‐text data: Exemplar investigation of factors influencing health behaviours and wellbeing during the COVID‐19 pandemic
Full text linkObjectives: Investigate the use of machine learning to expedite thematic analysis of qualitative data concerning factors that influenced health behaviours and wellbeing during the COVID-19 pandemic. Design: Qualitative investigation using Machine-Assisted Topic Analysis (MATA) of free-text data collected from a prospective cohort. Methods: Free-text survey data (2177 responses from 762 participants) of influences on health behaviours and wellbeing were collected among UK participants recruited online, using Qualtrics at 3, 6, 12 and 24 months after the COVID-19 pandemic started. MATA, which employs structural topic modelling (STM), was used (in R) to discern latent topics within the responses. Two researchers independently labelled topics and collaboratively organized them into themes, with ‘sense checking’ from two additional researchers. Plots and rankings were generated, showing change in topic prevalence by time. Total researcher time to complete analysis was collated. Results: Fifteen STM-generated topics were labelled and integrated into six themes: the influences of and impacts on (1) health behaviours, (2) physical health (3) mood and (4) how these interacted, partly moderated by (5) external influences of control and (6) reflections on wellbeing and personal growth. Topic prevalence varied meaningfully over time, aligning with changes in the pandemic context. Themes were generated (excluding write-up) with 20 h combined researcher time. Conclusions: MATA shows promise as a resource-saving method for thematic analysis of large qualitative datasets whilst maintaining researcher control and insight. Findings show the interconnection between health behaviours, physical health and wellbeing over the pandemic, and the influence of control and reflective processes
Applying machine-learning to rapidly analyze large qualitative text datasets to inform the COVID-19 pandemic response: comparing human and machine-assisted topic analysis techniques
No full textIntroduction: machine-assisted topic analysis (MATA) uses artificial intelligence methods to help qualitative researchers analyze large datasets. This is useful for researchers to rapidly update healthcare interventions during changing healthcare contexts, such as a pandemic. We examined the potential to support healthcare interventions by comparing MATA with “human-only” thematic analysis techniques on the same dataset (1,472 user responses from a COVID-19 behavioral intervention).Methods: in MATA, an unsupervised topic-modeling approach identified latent topics in the text, from which researchers identified broad themes. In human-only codebook analysis, researchers developed an initial codebook based on previous research that was applied to the dataset by the team, who met regularly to discuss and refine the codes. Formal triangulation using a “convergence coding matrix” compared findings between methods, categorizing them as “agreement”, “complementary”, “dissonant”, or “silent”.Results: human analysis took much longer than MATA (147.5 vs. 40 h). Both methods identified key themes about what users found helpful and unhelpful. Formal triangulation showed both sets of findings were highly similar. The formal triangulation showed high similarity between the findings. All MATA codes were classified as in agreement or complementary to the human themes. When findings differed slightly, this was due to human researcher interpretations or nuance from human-only analysis.Discussion: results produced by MATA were similar to human-only thematic analysis, with substantial time savings. For simple analyses that do not require an in-depth or subtle understanding of the data, MATA is a useful tool that can support qualitative researchers to interpret and analyze large datasets quickly. This approach can support intervention development and implementation, such as enabling rapid optimization during public health emergencies
The public health potential of mobile applications to increase physical activity
Full text linkBackground: Physical activity (PA) is an important behavioural determinant of morbidity and mortality and is a public health priority. The accessibility, convenience and wide reach of mobile applications (apps) makes these digital interventions a potential mode for delivering PA interventions at scale. At the end of 2017 there were 325,000 health apps available publicly, with “fitness” apps being the largest category of all health apps. However, most apps on the market have not been evaluated and little is known about their quality. Aim: This PhD investigated the public health potential of publicly available PA apps. Methods: The following studies were conducted: 1) a review and content analysis of the most popular PA apps on the market to assess their quality, defined as safety, likely efficacy and positive user experience; 2) a study using regression models to determine the association between popularity and quality of those apps; 3) a feasibility crossover trial assessing two apps for increasing PA; and 4) a qualitative study assessing the acceptability of the trial procedures and exploring the experiences of the two PA apps. Results: Popular apps had high usability but there were issues around their safety and likely efficacy. Popularity was not associated with likely efficacy. The feasibility trial and the qualitative study showed that such a trial would be feasible and acceptable to participants. The enablers and barriers to increasing exercise using the apps were identified. Conclusion: The discrepancy between quality and popularity represents a missed opportunity for behaviour change interventions. Hence, the public health impact of PA apps is unlikely to be achieved when market forces “prescribe” what is used by the public. The motivation to use the apps varied substantially and it is important to identify when, for whom, and in what context PA apps are most likely to facilitate behaviour change
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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