Using behavioral theory and shared decision-making to understand clinical trial recruitment: interviews with trial recruiters
Notice bibliographique
Résumé
BACKGROUND: Clinical trial recruitment is a continuing challenge for medical researchers. Previous efforts to improve study recruitment have rarely been informed by theories of human decision making and behavior change. We investigate the trial recruitment strategies reported by study recruiters, guided by two influential theoretical frameworks: shared decision-making (SDM) and the Theoretical Domains Framework (TDF) in order to explore the utility of these frameworks in trial recruitment. METHODS: We interviewed all nine active study recruiters from a multi-site, open-label pilot trial assessing the feasibility of a large-scale randomized trial. Recruiters were primarily nurses or master's-level research assistants with a range of 3 to 30 years of experience. The semi-structured interviews included questions about the typical recruitment encounter, questions concerning the main components of SDM (e.g. verifying understanding, directive vs. non-directive style), and questions investigating the barriers to and drivers of their recruitment activities, based on the TDF. We used directed content analysis to code quotations into TDF domains, followed by inductive thematic analysis to code quotations into sub-themes within domains and overarching themes across TDF domains. Responses to questions related to SDM were aggregated according to level of endorsement and informed the thematic analysis. RESULTS: The analysis helped to identify 28 sub-themes across 11 domains. The sub-themes were organized into six overarching themes: coordinating between people, providing guidance to recruiters about challenges, providing resources to recruiters, optimizing study flow, guiding the recruitment decision, and emphasizing the benefits to participation. The SDM analysis revealed recruiters were able to view recruitment interactions as successful even when enrollment did not proceed, and most recruiters took a non-directive (i.e. providing patients with balanced information on available options) or mixed approach over a directive approach (i.e. focus on enrolling patient in study). Most of the core SDM constructs were frequently endorsed. CONCLUSIONS: Identified sub-themes can be linked to TDF domains for which effective behavior change interventions are known, yielding interventions that can be evaluated as to whether they improve recruitment. Despite having no formal training in shared decision-making, study recruiters reported practices consistent with many elements of SDM. The development of SDM training materials specific to trial recruitment could improve the informed decision-making process for patients.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,071 | 0,173 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».