Systematic review of qualitative literature on occupational health and safety legislation and regulatory enforcement planning and implementation
Bibliographic record
Abstract
OBJECTIVE: The ability of occupational health and safety (OHS) legislation and regulatory enforcement to prevent workplace injuries and illnesses is contingent on political, economic, and organizational conditions. This systematic review of qualitative research articles considers how OHS legislation and regulatory enforcement are planned and implemented. METHODS: A comprehensive search of peer-reviewed, English-language articles published between 1990 and 2013 yielded 11 947 articles. We identified 34 qualitative articles as relevant, 18 of which passed our quality assessment and proceeded to meta-ethnographic synthesis. RESULTS: The synthesis yielded four main themes: OHS regulation formation, regulation challenges, inspector organization, and worker representation in OHS. It illuminates how OHS legislation can be based on normative suppositions about worker and employer behavior and shaped by economic and political resources of parties. It also shows how implementation of OHS legislation is affected by "general duty" law, agency coordination, resourcing of inspectorates, and ability of workers to participate in the system. CONCLUSIONS: The review identifies methodological gaps and identifies promising areas for further research in "grey" zones of legislation implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".