Have COVID-19 Stimulus Packages Mitigated the Negative Health Impacts of Pandemic-Related Job Losses? A Systematic Review of Global Evidence from the First Year of the Pandemic
Bibliographic record
Abstract
Social protection can buffer the negative impacts of unemployment on health. Have stimulus packages introduced during the COVID-19 pandemic mitigated potential harms to health from unemployment? We performed a systematic review of the health effects of job loss during the first year of the pandemic. We searched three electronic databases and identified 49 studies for inclusion. Three United States-based studies found that stimulus programs mitigated the impact of job loss on food security and mental health. Furloughs additionally appeared to reduce negative impacts when they were paid. However, despite the implementation of large-scale stimulus packages to reduce economic harms, we observed a clear pattern that job losses were nevertheless significantly associated with negative impacts, particularly on mental health, quality of life, and food security. We also observe suggestive evidence that COVID-related job loss was associated with child maltreatment, worsening dental health, and poor chronic disease outcomes. Overall, although we did find evidence that income-support policies appeared to help protect people from the negative health consequences of pandemic-related job loss, they were not sufficient to fully offset the threats to health. Future research should ascertain how to ensure adequate access to and generosity of social protection programs during epidemics and economic downturns.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".