Shared decision-making and behaviour change collide: an analysis of consultations discussing clinical trial recruitment
Notice bibliographique
Résumé
BACKGROUND: Recruitment to clinical trials is often challenging, and evidence on effective recruitment strategies remains limited. Framing trial recruitment as a behaviour, amenable to change, enables the application of behavioural science frameworks to better understand and potentially improve recruitment processes. Since trial participation is also a preference-sensitive decision, shared decision-making (SDM) may further enhance ethical recruitment. The present study aimed to explore how behavioural taxonomies and SDM frameworks can be applied to recruitment consultations to identify behaviour change techniques (BCTs) and assess the extent of SDM. METHODS: A secondary analysis was conducted on 53 audio-recorded consultations; consultations from 3 trials in oncology and gastroenterology were sampled. The action, actor, context, target, and time (AACTT) framework was used to define recruitment behaviours, which were then coded using the Behaviour Change Technique Taxonomy v1 (BCTTv1). SDM was assessed using the 5-item 'Observing Patient Involvement' (OPTION5) tool, which evaluates patient involvement in decision-making. Descriptive statistics and frequency counts were used to analyse the data. RESULTS: Twenty-one of the 93 BCTs in the BCTTv1 were identified across all consultations. The most frequently coded BCTs were 5.1 (information about health consequences) and 5.3 (information about social and environmental consequences), both present in all consultations. No substantial difference in the total number of BCTs was observed between trial consenters (M = 6.8, SD = 1.5) and decliners (M = 6.4, SD = 2.1). However, some techniques showed variability: BCT 7.1 (prompts and cues) appeared more frequently in consultations with consenters (84%) than decliners (38%), while BCT 1.4 (action planning) was more frequent in decliners (44%) than consenters (27%). SDM, as measured by OPTION5, was low overall, with a mean score of 27.7 (SD = 12.2) out of 100, with no significant differences across trials or participant groups. CONCLUSIONS: This study demonstrates the feasibility of applying behavioural science and SDM frameworks to analyse trial recruitment consultations. While a range of BCTs were identified, SDM efforts were generally low. Recruitment practices may benefit from more deliberate consideration of these techniques and greater emphasis on shared decision-making to support informed choices.
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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 | 0,034 | 0,238 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».