MétaCan
Menu
Retour à la cohorte
Enregistrement W6959032243 · doi:10.6084/m9.figshare.c.7091048.v1

Distributed Medical Education (DME) in psychiatry: perspectives on facilitators, obstacles, and factors affecting psychiatrists' willingness to engage in teaching activities

2024· other· en· W6959032243 sur OpenAlexaffabout

Notice bibliographique

RevueFigshare · 2024
Typeother
Langueen
DomaineSocial Sciences
ThématiqueLegal and Regulatory Analysis
Établissements canadiensUniversity of AlbertaDalhousie University
Organismes subventionnairesnon disponible
Mots-clésNova scotiaDescriptive statisticsBridge (graph theory)Logistic regressionTraining (meteorology)Mental healthFaculty developmentDescriptive research

Résumé

récupéré en direct d'OpenAlex

Abstract Background Distributed Medical Education (DME), a decentralized model focused on smaller cities and communities, has been implemented worldwide to bridge the gap in psychiatric education. Faculty engagement in teaching activities such as clinical teaching, supervision, and examinations is a crucial aspect of DME sites. Implementing or expanding DME sites requires careful consideration to identify enablers that contribute to success and barriers that need to be addressed. This study aims to examine enablers, barriers, and factors influencing psychiatrists' willingness to start or continue participating in teaching activities within Dalhousie University's Faculty of Medicine DME sites in two provinces in Atlantic Canada. Methodology This cross-sectional study was conducted as part of an environmental scan of Dalhousie Faculty of Medicine’s DME programs in Nova Scotia (NS) and New Brunswick (NB), Canada. In February 2023, psychiatrists from seven administrative health zones in these provinces anonymously participated in an online survey. The survey, created with OPINIO, collected data on sociodemographic factors, practice-related characteristics, medical education, and barriers to teaching activities. Five key outcomes were assessed, which included psychiatrists' willingness to engage in (i) clinical training and supervision, (ii) lectures or skills-based teaching, (iii) skills-based examinations, (iv) training and supervision of Canadian-trained psychiatrists, and (v) training and supervision of internationally trained psychiatrists. The study employed various statistical analyses, including descriptive analysis, chi-square tests, and logistic regression, to identify potential predictors associated with each outcome variable. Results The study involved 60 psychiatrists, primarily male (69%), practicing in NS (53.3%), with international medical education (69%), mainly working in outpatient services (41%). Notably, 60.3% lacked formal medical education training, yet they did not perceive the lack of training as a significant barrier, but lack of protected time as the main one. Despite this, there was a strong willingness to engage in teaching activities, with an average positive response rate of 81.98%. The lack of protected time for teaching/training was a major barrier reported by study participants. Availability to take the Royal College of Physicians and Surgeons of Canada Competency by Design training was the main factor associated with psychiatrists' willingness to participate in the five teaching activities investigated in this study: willingness to participate in clinical training and supervision of psychiatry residents (p = .01); provision of lectures or skills-based teaching for psychiatry residents (p < .01); skills-based examinations of psychiatry residents (p < .001); training/supervision of Canadian-trained psychiatrists (p < .01); and training and supervision of internationally trained psychiatrists (p < .01). Conclusion The study reveals a nuanced picture regarding psychiatrists' engagement in teaching activities at DME sites. Despite a significant association between interest in formal medical education training and willingness to participate in teaching activities, clinicians do not consider the lack of formal training as a barrier. Addressing this complexity requires thoughtful strategies, potentially involving resource allocation, policy modifications, and adjustments to incentive structures by relevant institutions.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,680
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0150,000

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,319
Écart entre enseignants0,305 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueFigshareMême sujetLegal and Regulatory AnalysisTravaux en français237 207