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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».