Health worker education during the COVID-19 pandemic: global disruption, responses and lessons for the future—a systematic review and meta-analysis
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".