An unexpected transition to virtual care: family medicine residents’ experience during the COVID-19 pandemic
Notice bibliographique
Résumé
BACKGROUND: The global COVID-19 pandemic led to rapid changes in both medical care and medical education, particularly involving the rapid substitution of virtual solutions for traditional face-to-face appointments. There is a need for research into the effects and impacts of such changes. The objective of this article investigates the perspectives of Family Medicine Residents in one university program in order to understand the impact of this transition to virtual care and learning. METHODS: This is a qualitative focus group study. Four focus groups, stratified by site type (Rural = 1; Semi-Urban = 1; Urban = 2) were conducted, with a total of 25 participants. Participants were either first or second-year Residents in Family Medicine. Focus group recordings were analyzed thematically, based upon a five-level socio-ecological model (individual, family, organization, community, environment and policy context). RESULTS: Two main themes were identified: (1) Residents' experiences of Virtual Learning and Virtual Care, and (2) Living and Learning in Pandemic Times. In the first theme, Residents reported challenges both individually, in their family context, and in their training organizations. Of particular concern was the loss of hands-on experience with clinical skills such as conducting physical examinations. In the second theme, Residents reported disruption of self-care routines and family life. These Residents were unable to engage in the relationships outside of the workplace with their preceptors and peers which they had expected, and which play key roles in social support as well as in future decisions about practice location. CONCLUSIONS: While many patients appreciated virtual care, in the eyes of these Residents it is not the ideal modality for learning the practice of Family Medicine, and they awaited a return to normal times. Despite this, the pandemic has pointed out important ways in which residency training needs to adapt to an evolving world.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».