Assessing the hidden curriculum in medical education: a scoping review and residency program’s reflection
Notice bibliographique
Résumé
Background: While the hidden curriculum (HC) is becoming recognized as an important component of medical education, ideal methods of assessing the HC are not well known. The aim of this study was to review the literature for methods of assessing the HC in the context of healthcare education. Methods: We conducted a scoping review on methods to measure or assess the HC in accordance with the JBI Manual for Evidence Synthesis. Ovid MEDLINE, Ovid EMBASE, and ProQuest ERIC databases were searched from inception until August 2023. Studies which focused on healthcare education, including medicine, as well as other professions such as nursing, social work, pharmacy were included. We then obtained stakeholder feedback utilizing the results of this review to inform the ongoing HC assessment process within our own medical education program. Results: Of 141 studies included for full text review, 41 were included for analysis and data extraction. Most studies were conducted in North America and qualitative in nature. Physician education was best represented with most studies set in undergraduate medical education (n = 21, 51%). Assessment techniques included interviews (n = 19, 46%), cross-sectional surveys (n = 14, 34%), written reflections (n = 7, 17%), and direct observation of the working environment (n = 2, 5%). While attempts to create standardized HC evaluation methods were identified, there were no examples of implementation into an educational program formally or longitudinally. No studies reported on actions taken based on evaluation results. Confidential stakeholder feedback was obtained from postgraduate medical learners in our program, and this feedback was then used to modify our longitudinal HC assessment process. Conclusions: While the HC has as increasing presence in the medical education community, the ideal way to practically assess it within a healthcare education context remains unclear. We described the HC assessment process utilized at our program, which may be informative for other institutions attempting to implement a similar technique. Future attempts and studies would benefit from reporting longitudinal data and impacts of assessment results
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 | 0,006 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».