Working in the dead of night: exploring the transition to after‐hours duty
Notice bibliographique
Résumé
CONTEXT: Transitions, although often difficult, represent integral components of medical training. New postgraduate trainees (first-year residents) find themselves in an especially challenging transition as they are expected to fulfil both learning and service expectations concurrently. Workplace learning theory has been suggested as a lens through which to understand this unique educational, yet service-oriented, role. This tension may be further amplified overnight when residents are on-call with little to no support. OBJECTIVES: The aims of this study were to explore the transition from medical student to resident with respect to the on-call experience, and to provide theory-based suggestions to enhance learning during this unique transition. METHODS: We conducted an interpretivist qualitative study by interviewing eight medical students and 10 first-year residents from six different specialty training programmes across four academic sites. Each semi-structured interview was transcribed verbatim and anonymised. Resident interview transcripts were initially coded for major themes, after which medical student interview transcripts were coded for consistencies and discrepancies. RESULTS: Four interrelated themes were identified in students' and residents' descriptions of on-call experiences: (i) shift in responsibility; (ii) supervisory support; (iii) contextual conditions, and (iv) clarity of expectations. Generally, students were not able to anticipate the challenges they would face as residents on-call, and residents perceived the transition as sudden with little emphasis placed on learning. CONCLUSIONS: First-year residents face multiple challenges during on-call, which may prevent optimal learning in this setting. These challenges are amplified by the large gap between the respective roles of medical students and residents. We identified promoters of and barriers to effective learning in this environment and, by using workplace learning theory, provide recommendations for how we might be able to enhance medical students' preparation for and first-year residents' learning during experiences of being on-call.
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 | 0,002 | 0,003 |
| 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 | 0,000 |
| 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 ».