Effective accreditation in postgraduate medical education: from process to outcomes and back
Notice bibliographique
Résumé
BACKGROUND: The accreditation of medical educational programs is thought to be important in supporting program improvement, ensuring the quality of the education, and promoting diversity, equity, and population health. It has long been recognized that accreditation systems will need to shift their focus from processes to outcomes, particularly those related to the end goals of medical education: the creation of broadly competent, confident professionals and the improvement of health for individuals and populations. An international group of experts in accreditation convened in 2013 to discuss this shift. MAIN TEXT: Participants unequivocally supported the inclusion of more outcomes-based criteria in medical education accreditation, specifically those related to the societal accountability of the institutions in which the education occurs. Meaningful and feasible outcome metrics, however, are hard to identify. They are regionally variable, often temporally remote from the educational program, difficult to measure, and susceptible to confounding factors. The group identified the importance of health outcomes of the clinical milieu in which education takes place in influencing outcomes of its graduates. The ability to link clinical data with individual practice over time is becoming feasible with large repositories of assessment data linked to patient outcomes. This was seen as a key opportunity to provide more continuous oversight and monitoring of program impact. The discussants identified several risks that might arise should outcomes measures completely replace process issues. Some outcomes can be measured only by proxy process elements, and some learner experience issues may best be measured by such process elements: in brief, the "how" still matters. CONCLUSIONS: Accrediting bodies are beginning to view the use of practice outcome measures as an important step toward better continuous educational quality improvement. The use of outcomes will present challenges in data collection, aggregation, and interpretation. Large datasets that capture clinical outcomes, experience of care, and health system performance may enable the assessment of multiple dimensions of program quality, assure the public that the social contract is being upheld, and allow identification of exemplary programs such that all may improve. There remains a need to retain some focus on process, particularly those related to the learner experience.
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 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».