Distance Learning During the COVID-19 Lockdown and Self-Assessed Competency Development Among Radiology Residents in China: Cross-Sectional Survey
Notice bibliographique
Résumé
Background: During the COVID-19 lockdown, it was difficult for residency training programs to conduct on-site, hands-on training. Distance learning, as an alternative to in-person training, could serve as a viable option during this challenging period, but few studies have assessed its role. Objective: This study aims to investigate the impact of distance learning during the lockdown on residents' self-assessed competency development and to explore the moderating effect of poor mental health on the associations. It is hypothesized that radiology residents who were trained through distance learning during the lockdown were more likely to report higher self-assessed competency compared to those who did not receive organized, formal training. Methods: A cross-sectional survey was conducted in 2021 among all of the radiology residents in 407 radiology residency programs across 31 provinces of China. To estimate the long-term outcomes of radiology residents' training after the initial COVID-19 outbreak, this study measured 6 core competencies developed by the US Accreditation Council for Graduate Medical Education reported by radiology residents. Multiple linear regression and moderating effect analysis were conducted to examine the associations between distance learning, mental health status, and self-assessed competencies. Mental health status moderated the association between distance learning and self-assessed competency of radiology residents. Results: A total of 2381 radiology residents (29.7% of the 8,008 nationwide) met the inclusion criteria and were included in the analysis. Among them, 71.4% (n=1699) received distance learning during the COVID-19 lockdown, and 73.2% (n=1742) reported mental health struggles ranging in severity from slight to extremely severe. Radiology residents who were trained through distance learning (β=0.35, 90% CI 0.24-0.45) were more likely to report higher self-assessed competencies. This was particularly true for the competency of "interpersonal and communication skills" (β=0.55, 90% CI 0.39-0.70). Whereas, the competency of "patient care and technical skills" (β=0.14, 90% CI 0.01-0.26) benefited the least from distance learning. Poor mental health significantly moderated the relationship between distance learning and competency (β=-0.15, 90% CI -0.27 to -0.02). Conclusions: Distance learning, a means of promoting enabling environments during the COVID-19 lockdown, serves its purpose and helps generally improve residents' self-assessed competencies, though different competency domains benefit unequally. The impact of mental health status calls for special attention so that distance learning can fulfill its potential.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».