Characterizing the Gaps Between Best-Practice Implementation Strategies and Real-world Implementation: Qualitative Study Among Family Physicians Who Engaged With Audit and Feedback Reports
Notice bibliographique
Résumé
BACKGROUND: In Ontario, Canada, a government agency known as Ontario Health is responsible for making audit and feedback reports available to all family physicians to encourage ongoing quality improvement. The confidential report provides summary data on 3 key areas of practice: safe prescription, cancer screening, and diabetes management. OBJECTIVE: This report was redesigned to improve its usability in line with evidence. The objective of this study was to explore how the redesign was perceived, with an emphasis on recipients' understanding of the report and their engagement with it. METHODS: We conducted qualitative semistructured interviews with family physicians who had experience with both versions of the report recruited through purposeful and snowball sampling. We analyzed the transcripts following an emergent and iterative approach. RESULTS: Saturation was reached after 17 family physicians participated. In total, 2 key themes emerged as factors that affected the perceived usability of the report: alignment between the report and the recipients' expectations and capacity to engage in quality improvement. Family physicians expected the report and its quality indicators to reflect best practices and to be valid and accurate. They also expected the report to offer feedback on the clinical activities they perceived to be within their control to change. Furthermore, family physicians expected the goal of the report to be aligned with their perspective on feasible quality improvement activities. Most of these expectations were not met, limiting the perceived usability of the report. The capacity to engage with audit and feedback was hindered by several organizational and physician-level barriers, including the lack of fit with the existing workflow, competing priorities, time constraints, and insufficient skills for bridging the gaps between their data and the corresponding desired actions. CONCLUSIONS: Despite recognized improvements in the design of the report to better align with best practices, it was not perceived as highly usable. Improvements in the presentation of the data could not overcome misalignment with family physicians' expectations or the limited capacity to engage with the report. Integrating iterative evaluations informed by user-centered design can complement evidence-based guidance for implementation strategies. Creating a space for bringing together audit and feedback designers and recipients may help improve usability and effectiveness.
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 | 0,043 | 0,079 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,010 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».