Changing or validating physician opioid prescribing behaviors through audit and feedback and academic detailing interventions in primary care
Notice bibliographique
Résumé
Background In Ontario, Canada, province-wide initiatives supporting safer opioid prescribing in primary care include voluntary audit and feedback reports and academic detailing. In this process evaluation, we aimed to determine the fidelity of delivery and receipt of the interventions, the observed change strategies used by physicians, potential mechanisms of action, and how complementary the initiatives can be to each other. Method Semi-structured interviews were conducted with academic detailers and with physicians who received both interventions. Academic detailer interviews were coded using the Behavior Change Technique Taxonomy; physician interviews were coded to the Theoretical Domain Framework. Change strategies were summarized based on academic detailer intentions and physician-reported practice changes. Potential mechanisms of action were identified using the Theories and Techniques Tool and the literature. Patient partners informed the interpretation of results through ongoing group discussions of preliminary findings. Results Interviews were conducted with eight academic detailers and 12 physicians. Change strategies described by academic detailers to support physicians’ opioid prescribing included problem solving, instructions on how to perform the behavior, adding objects to the environment, credible source, shaping knowledge, and social support. Physicians mentioned that academic detailing validated current opioid practices or increased their belief about capabilities and their intentions, mediated by increased skills and the impact of environmental context and resources. Potential mechanisms of action included behavioral regulation, behavioral cueing, and general attitudes/beliefs. On its own, receiving the audit and feedback report did not lead to changes in beliefs about prescribing practices; however, for some physicians, it provided validation and reassurance. Physicians saw unrealized potential for complementarity. Conclusions New interventions are often implemented in a complex ecosystem with other competing interventions. In this study, we show how examining the fidelity of the intervention from initial design through to delivery can identify opportunities for potential optimization.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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; un appel candidat d’une seule tête enseignante, 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 ».