Detecting Conversation Topics in Recruitment Calls of African American Participants to the All of Us Research Program Using Machine Learning: Model Development and Validation Study Article Swipe
Priscilla Pemu
,
Michael Prude
,
Atuarra McCaslin
,
Elizabeth Ojemakinde
,
Christopher Awad
,
Kelechi Igwe
,
Anny Rodriguez
,
Jasmine C. Moore
,
Muhammed Y. Idris
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.2196/65320
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.2196/65320
Background Advancements in science and technology can exacerbate health disparities, particularly when there is a lack of diversity in clinical research, which limits the benefits of innovations for underrepresented communities. Programs like the All of Us Research Program (AoURP) are actively working to address this issue by ensuring that underrepresented populations are represented in biomedical research, promoting equitable participation, and advancing health outcomes for all. African American communities have been particularly underrepresented in clinical research, often due to historical instances of research misconduct, such as the Tuskegee Syphilis Study, which have deeply impacted trust and willingness to participate in research studies. With the US population becoming increasingly diverse, it is crucial that clinical research studies reflect this diversity to improve health outcomes. However, limited data and small sample sizes in qualitative studies on the inclusion of underrepresented groups hinder progress in this area. Objective The goal of this paper is to analyze recruitment conversations between research assistants (RAs) and potential participants in the AoURP to identify key topics that influence enrollment. By examining these interactions, we aim to provide insights that can improve engagement strategies and recruitment practices for underrepresented groups in biomedical research. Methods Our study design was an observational, retrospective approach using machine learning for content analysis. Specifically, we used structural topic modeling to identify and compare latent topics of conversation in recruitment calls by Morehouse School of Medicine RAs between February 2021 and April 2022 by estimating expected topic proportions in the corpus as a function of enrollment and participation in AoURP. Results In total, our model estimated 45 topics of which 12 coherent topics were identified. Notable topics, that were more likely to occur in conversations between RAs and participants that enrolled and participated, include closing or following up to schedule an appointment, COVID-19 protocols for in-person visits, explaining precision medicine and the need for representation, and working through objections, including concerns about costs, insurance, care changes, and health fears. Topics among potential participants who did not enroll include technical challenges and describing physical measurement visits (eg, collection of basic physical data, such as height, weight, and blood pressure). Conclusions Using an approach that leverages machine learning to identify topical structure and themes with limited human subjectivity is a promising strategy to identify gaps in, and opportunities to improve, the recruitment of underserved communities into clinical trials.
Related Topics
Concepts
Conversation
Health equity
Underrepresented Minority
Diversity (politics)
Medical education
Inclusion (mineral)
Population
Psychology
Observational study
Research design
Medicine
Public relations
Political science
Sociology
Public health
Social psychology
Social science
Nursing
Environmental health
Law
Pathology
Communication
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/65320
- OA Status
- gold
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407547006
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4407547006Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/65320Digital Object Identifier
- Title
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Detecting Conversation Topics in Recruitment Calls of African American Participants to the All of Us Research Program Using Machine Learning: Model Development and Validation StudyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-02-14Full publication date if available
- Authors
-
Priscilla Pemu, Michael Prude, Atuarra McCaslin, Elizabeth Ojemakinde, Christopher Awad, Kelechi Igwe, Anny Rodriguez, Jasmine C. Moore, Muhammed Y. IdrisList of authors in order
- Landing page
-
https://doi.org/10.2196/65320Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.2196/65320Direct OA link when available
- Concepts
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Conversation, Health equity, Underrepresented Minority, Diversity (politics), Medical education, Inclusion (mineral), Population, Psychology, Observational study, Research design, Medicine, Public relations, Political science, Sociology, Public health, Social psychology, Social science, Nursing, Environmental health, Law, Pathology, CommunicationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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26Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.research | 81, 99, 113, 155 |
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| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
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