Occupational health and safety regulatory interventions to improve the work environment: An evidence and gap map of effectiveness studies
Bibliographic record
Abstract
Background: Unsafe and unhealthy working conditions lead to injuries and financial losses across the globe, resulting in a need for research into effective work environment interventions. Objectives: The objective of this evidence and gap map (EGM) is to provide an overview of existing systematic reviews and primary studies examining the effects of occupational health and safety regulatory interventions. Search Methods: Relevant studies are identified through searches in published and unpublished literature performed up to January 2023. Selection Criteria: The population for this EGM is workers above the age of 15 and their workplaces within the OECD. We include randomised controlled trials, non-randomised studies with a comparison of two or more groups of participants, and systematic reviews of effects. Data Collection and Analysis: The map has been populated based on information about interventions and outcomes, study design, OECD country, and publication status. We have performed critical appraisal of included systematic reviews using an adjusted version of the AMSTAR-2 tool. Main Results: The included studies for this report consist of six systematic reviews, 28 primary effect studies, and three on-going studies. The interactive map shows that the largest cluster of studies is located in the inspection activity domain, while the sickness absence outcome domain and the intervention categories for training initiatives and formulation of regulatory standards are only scarcely populated. Additionally, the AMSTAR-appraisal suggests a lack of rigorous systematic reviews and meta-analyses. Authors’ Conclusions: More research in the form of primary studies and rigorous systematic reviews is needed to provide stakeholders with better guidance as to what constitutes the most efficient regulatory approaches to improve the work environment.
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 | 0.135 | 0.345 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.055 | 0.038 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".