An Umbrella Review of the Work and Health Impacts of Working in an Epidemic/Pandemic Environment
Bibliographic record
Abstract
This umbrella review of reviews examined the evidence on the work and health impacts of working in an epidemic/pandemic environment, factors associated with these impacts, and risk mitigation or intervention strategies that address these factors. We examined review articles published in MEDLINE, PsycINFO and Embase between 2000 and 2020. Data extracted from the included reviews were analyzed using a narrative synthesis. The search yielded 1524 unique citations, of which 31 were included. Included studies were focused on health care workers and the risk of infection to COVID-19 or other respiratory illnesses, mental health outcomes, and health care workers' willingness to respond during a public health event. Reviews identified a variety of individual, social, and organizational factors associated with these work and health outcomes as well as risk mitigation strategies that addressed study outcomes. Only a few reviews examined intervention strategies in the workplace such as physical distancing and quarantine, and none included long-term outcomes of exposure or work during an epidemic/pandemic. Findings suggest a number of critical research and evidence gaps, including the need for reviews on occupational groups potentially exposed to or impacted by the negative work and health effects of COVID-19 in addition to health care workers, the long-term consequences of transitioning to the post-COVID-19 economy on work and health, and research with an equity or social determinants of health lens.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".