Mental health outcomes in health care providers during the COVID-19 pandemic: an umbrella review
Bibliographic record
Abstract
As we head into the third year of the COVID-19 pandemic, there is an increasing need to consider the long-term mental health outcomes of health care workers (HCWs) who have experienced overwhelming work pressure, economic and social deprivation, burnout, and post-traumatic stress disorder (PTSD). This scoping umbrella review summarizes the mental health outcomes of published evidence syntheses on HCWs worldwide. We analyzed 39 evidence syntheses representing the findings from 1297 primary studies. We found several persistent fears and concerns (job-related fears, fear of stigmatization, worries about the pandemic, and infection-related fears) that shaped HCW experiences in delivering health care. We also describe several risk factors (job-related, social factors, poor physical and mental health, and inadequate coping strategies) and protective factors (individual and external factors). This is the first scoping umbrella review comprehensively documenting the various risk and protective factors that HCWs have faced during the COVID-19 pandemic. HCWs continue to fear the risk that they may infect their family and friends since they regularly interact with COVID-19 patients. This places HCWs in a precarious situation requiring them to balance risk to their family and friends and potential social deprivation from isolation.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".