Initiatives Addressing Precarious Employment and Its Effects on Workers’ Health and Well-Being: A Systematic Review
Bibliographic record
Abstract
The prevalence of precarious employment has increased in recent decades and aspects such as employment insecurity and income inadequacy have intensified during the COVID-19 pandemic. The purpose of this systematic review was to identify, appraise, and synthesise existing evidence pertaining to implemented initiatives addressing precarious employment that have evaluated and reported health and well-being outcomes. We used the PRISMA framework to guide this review and identified 11 relevant initiatives through searches in PubMed, Scopus, Web of Science, and three sources of grey literature. We found very few evaluated interventions addressing precarious employment and its impact on the health and well-being of workers globally. Ten out of 11 initiatives were not purposefully designed to address precarious employment in general, nor specific dimensions of it. Seven out of 11 initiatives evaluated outcomes related to the occupational health and safety of precariously employed workers and six out of 11 evaluated worker health and well-being outcomes. Most initiatives showed the potential to improve the health of workers, although the evaluation component was often described with less detail than the initiative itself. Given the heterogeneity of the 11 initiatives regarding study design, sample size, implementation, evaluation, economic and political contexts, and target population, we found insufficient evidence to compare outcomes across types of initiatives, generalize findings, or make specific recommendations for the adoption of initiatives.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".