Health worker education during the COVID-19 pandemic: global disruption, responses and lessons for the future—a systematic review and meta-analysis
Notice bibliographique
Résumé
Abstract Background This systematic review and meta-analysis identified early evidence quantifying the disruption to the education of health workers by the COVID-19 pandemic, ensuing policy responses and their outcomes. Methods Following a pre-registered protocol and PRISMA/AMSTAR-2 guidelines, we systematically screened MEDLINE, EMBASE, Web of Science, CENTRAL, clinicaltrials.gov and Google Scholar from January 2020 to July 2022. We pooled proportion estimates via random-effects meta-analyses and explored subgroup differences by gender, occupational group, training stage, WHO regions/continents, and study end-year. We assessed risk of bias (Newcastle–Ottawa scale for observational studies, RοB2 for randomized controlled trials [RCT]) and rated evidence certainty using GRADE. Results Of the 171 489 publications screened, 2 249 were eligible, incorporating 2 212 observational studies and 37 RCTs, representing feedback from 1 109 818 learners and 22 204 faculty. The sample mostly consisted of undergraduates, medical doctors, and studies from institutions in Asia. Perceived training disruption was estimated at 71.1% (95% confidence interval 67.9–74.2) and learner redeployment at 29.2% (25.3–33.2). About one in three learners screened positive for anxiety (32.3%, 28.5–36.2), depression (32.0%, 27.9–36.2), burnout (38.8%, 33.4–44.3) or insomnia (30.9%, 20.8–41.9). Policy responses included shifting to online learning, innovations in assessment, COVID-19-specific courses, volunteerism, and measures for learner safety. For outcomes of policy responses, most of the literature related to perceptions and preferences. More than two-thirds of learners (75.9%, 74.2–77.7) were satisfied with online learning (postgraduates more than undergraduates), while faculty satisfaction rate was slightly lower (71.8%, 66.7–76.7). Learners preferred an in-person component: blended learning 56.0% (51.2–60.7), face-to-face 48.8% (45.4–52.1), and online-only 32.0% (29.3–34.8). They supported continuation of the virtual format as part of a blended system (68.1%, 64.6–71.5). Subgroup differences provided valuable insights despite not resolving the considerable heterogeneity. All outcomes were assessed as very-low-certainty evidence. Conclusion The COVID-19 pandemic has severely disrupted health worker education, inflicting a substantial mental health burden on learners. Its impacts on career choices, volunteerism, pedagogical approaches and mental health of learners have implications for educational design, measures to protect and support learners, faculty and health workers, and workforce planning. Online learning may achieve learner satisfaction as part of a short-term solution or integrated into a blended model in the post-pandemic future.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 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) | 0,016 | 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 ».