An umbrella review and meta-analysis of 87 meta-analyses examining healthcare workers' mental health during the COVID-19 pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, healthcare workers (HCWs) experienced several changes in their work (e.g., longer hours, new policies) that affected their mental health. In this study, an umbrella review and meta-analysis of meta-analyses was conducted to examine the prevalence of various mental health problems experienced by HCWs during the COVID-19 pandemic. We conducted a systematic review searching PubMed, EMBASE, PsycINFO, Cochrane Library, and Scopus databases (PROSPERO: CRD42022304823). We performed a meta-analysis to summarize prevalence of different mental health problems and examined whether these differed as a function of job category, sex/gender, sociodemographic index (SDI), and across time. Eighty-seven meta-analyses were included in the umbrella review and meta-analysis, including 1846 non-overlapping articles and 9,400,962 participants. The overall prevalence ratio for the different mental health outcomes ranged from 0.20 for PTSD (95 % CI: 0.16-0.25) to 0.44 for burnout (95 % CI: 0.32-0.56), with ratios for depressive symptoms, anxiety symptoms, psychological distress, perceived stress, sleep problems, and insomnia symptoms falling between these ranges. Follow-up analyses revealed little variation in outcomes across job category, and sex. Prevalence of mental health problems in HCWs was high during the pandemic. Administrators and policymakers worldwide need to address these growing problems through institutional policies and wellness programming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.058 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".