Comparing the application of two theoretical frameworks to describe determinants of adverse medical device event reporting: secondary analysis of qualitative interview data
Notice bibliographique
Résumé
BACKGROUND: Post-market surveillance of medical devices is reliant on physician reporting of adverse medical device events (AMDEs). Few studies have examined factors that influence whether and how physicians report AMDEs, an essential step in the development of behaviour change interventions. This study was a secondary analysis comparing application of the Theoretical Domains Framework (TDF) and the Tailored Implementation for Chronic Diseases (TICD) framework to identify potential behaviour change interventions that correspond to determinants of AMDE reporting. METHODS: A previous study involving qualitative interviews with Canadian physicians that implant medical devices identified themes reflecting AMDE reporting determinants. In this secondary analysis, themes that emerged from the primary analysis were independently mapped to the TDF and TICD. Determinants and corresponding intervention options arising from both frameworks (and both mappers) were compared. RESULTS: Both theoretical frameworks were useful for identifying interventions corresponding to behavioural determinants of AMDE reporting. Information or education strategies that provide evidence about AMDEs, and audit and feedback of AMDE data were identified as interventions to target the theme of physician beliefs; improving information systems, and reminder cues, prompts and awards were identified as interventions to address determinants arising from the organization or systems themes; and modifying financial/non-financial incentives and sharing data on outcomes associated with AMDEs were identified as interventions to target device market themes. Numerous operational challenges were encountered in the application of both frameworks including a lack of clarity about how directly relevant to themes the domains/determinants should be, how many domains/determinants to select, if and how to resolve discrepancies across multiple mappers, and how to choose interventions from among the large number associated with selected domains/determinants. CONCLUSIONS: Given discrepancies in mapping themes to determinants/domains and the resulting interventions offered by the two frameworks, uncertainty remains about how to choose interventions that best match behavioural determinants in a given context. Further research is needed to provide more nuanced guidance on the application of TDF and TICD for a broader audience, which is likely to increase the utility and uptake of these frameworks in practice.
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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 | 0,024 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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; 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 ».