Labour and social protection gaps impacting the health and well-being of workers in non-standard employment: An international comparative study
Bibliographic record
Abstract
BACKGROUND: World economies increasingly rely on non-standard employment arrangements, which has been linked to ill health. While work and employment conditions are recognized structural determinants of health and health equity, policies aiming to protect workers from negative implications predominantly focus on standard employment arrangements and the needs of workers in non-standard employment may be neglected. The aim of this study is to explore workers' experiences of gaps in labour regulations and social protections and its influence on their health and well-being across 6 countries with differing policy approaches: Belgium, Canada, Chile, Spain, Sweden, and the United States. METHODS: 250 semi-structured interviews with workers in non-standard employment were analyzed thematically using a multiple case-study approach. RESULTS: There are notable differences in workers' rights to protection across the countries. However, participants across all countries experienced similar challenges including employment instability, income inadequacy and limited rights and protection, due to policy-related gaps and access-barriers. In response, they resorted to individual resources and strategies, struggled to envision supportive policies, and expressed low expectations of changes by employers and policymakers. CONCLUSIONS: Policy gaps threaten workers' health and well-being across all study countries, irrespective of the levels of labour market regulations and social protections. Workers in non-standard employment disproportionately endure economic risks, which may increase social and health inequality. The study highlights the need to improve social protection for this vulnerable population.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".