Precarious employment and associated health and social consequences; a systematic review
Bibliographic record
Abstract
OBJECTIVE: This systematic review aims to identify, evaluate, and summarise the consequences of precarious employment. METHODS: We included studies published within the last ten years (Jan 2011-July 2021) that employed at least two of three key dimensions of precarious employment: employment insecurity, income inadequacy, and lack of rights and protection. RESULTS: Of the 4,947 initially identified studies, only five studies met our eligibility criteria. These five studies were of moderate quality as assessed by the Newcastle-Ottawa Scale. Our review found that the current literature predominantly defines precarity based on the single criterion of employment insecurity. Our review identified evidence for the negative consequences of precarious employment, including poorer workplace wellbeing, general health, mental health, and emotional wellbeing. The findings indicated an increase in the magnitude of these adverse outcomes with a higher degree of job precariousness. CONCLUSIONS: The rise of employment precariousness will likely continue to be a major issue in the coming years. More research is needed to inform effective policies and practices using a consensus definition of precarious employment. IMPLICATIONS FOR PUBLIC HEALTH: The presence of adverse effects of precarious employment suggests workplace initiatives are essential to mitigate the negative consequences of precarity.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| 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) | 0.004 | 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".