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Enregistrement W4292054930 · doi:10.9778/cmajo.20210199

Transitioning to virtual ambulatory care during the COVID-19 pandemic: a qualitative study of faculty and resident physician perspectives

2022· article· en· W4292054930 sur OpenAlexafffundvenueabout
Jessica S. S. Ho, Rebecca Leclair, Heather Braund, Jennifer A. Bunn, Ekaterina Kouzmina, Samantha Bruzzese, Sara Awad, Steve Mann, Ramana Appireddy, Boris Zevin

Notice bibliographique

RevueCMAJ Open · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueTelemedicine and Telehealth Implementation
Établissements canadiensQueen's University
Organismes subventionnairesSoutheastern Ontario Academic Medical OrganizationCanadian Institutes of Health ResearchPhysicians' Services Incorporated Foundation
Mots-clésCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)AmbulatoryFamily medicineMedicinePsychologyMedical emergencyMedical educationNursingVirologyInfectious disease (medical specialty)Internal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: The COVID-19 pandemic resulted in a rapid shift from in-person to virtual care delivery for many medical specialties across Canada. The purpose of this study was to explore the lived experiences of resident physicians and faculty related to teaching, learning and assessment during ambulatory virtual care encounters within the competency-based medical education model. METHODS: In this qualitative phenomenological study, we recruited resident physicians (postgraduate year [PGY] 1-5 trainees) and faculty from the Departments of Surgery and Medicine at Queen's University, Ontario, via purposive sampling. Participants were not required to have exposure to virtual care. Interviews were conducted from September 2020 to March 2021 by 1 researcher, and 2 researchers conducted focus groups via Zoom to explore participants' experiences with the transition to virtual care. These were audio-recorded and transcribed verbatim; qualitative data were analyzed thematically. RESULTS: There were 18 male and 19 female participants; 20 were resident physicians and 17 were faculty; 19 were from the Department of Surgery and 18 from the Department of Medicine. All faculty participants had participated in virtual care during ambulatory care; 2 PGY-1 residents in surgery had not actively participated in virtual care, although they had participated in clinics where faculty were using virtual care. The mean age of faculty participants was 38 (standard deviation [SD] 8.6) years, and the mean age of resident physicians was 29 (SD 5.4) years. Overall, 28 interviews and 4 focus groups (range 2-3 participants per group) were conducted, and 4 themes emerged: teaching and learning, assessment, logistical considerations, and suggestions. Barriers to teaching included the lack of direct observations and teaching time, and barriers to assessment included an absence of specific Entrustable Professional Activities (EPAs) and feedback focused on virtual care-related competencies. Logistical challenges included lack of technological infrastructure, insufficient private office space and administrative burdens. Both resident physicians and faculty did not foresee virtual care limiting resident physicians' ability to progress within competency-based medical education. Benefits of virtual care included increased accessibility to patients for follow-up visits, for disclosing patients' results and for out-of-town visits. Suggestions included faculty development, improved access to technology and space, educational guidelines for conducting virtual care encounters, and development of virtual care-specific competencies and EPAs. INTERPRETATION: In the postgraduate program we studied, virtual care imposed substantial barriers on teaching, learning and assessment during the first year of the COVID-19 pandemic. Adapting to new circumstances such as virtual care with suggestions from resident physicians and faculty may help to ensure the continuity of postgraduate medical education throughout the COVID-19 pandemic.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,144
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

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

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 Opus0,118
Tête enseignante GPT0,476
Écart entre enseignants0,358 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
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

Citations5
Publié2022
Routes d'admission4
Résumé présentoui

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