A scoping review to identify strategies that work to prevent four important occupational diseases
Bibliographic record
Abstract
BACKGROUND: Despite being largely preventable, many occupational diseases continue to be highly prevalent and extremely costly. Effective strategies are required to reduce their human, economic, and social impacts. METHODS: To better understand which approaches are most likely to lead to progress in preventing noise-related hearing loss, occupational contact dermatitis, occupational cancers, and occupational asthma, we undertook a scoping review and consulted with a number of key informants. RESULTS: We examined a total of 404 articles and found that various types of interventions are reported to contribute to occupational disease prevention but each has its limitations and each is often insufficient on its own. Our principal findings included: legislation and regulations can be an effective means of primary prevention, but their impact depends on both the nature of the regulations and the degree of enforcement; measures across the hierarchy of controls can reduce the risk of some of these diseases and reduce exposures; monitoring, surveillance, and screening are effective prevention tools and for evaluating the impact of legislative/policy change; the effect of education and training is context-dependent and influenced by the manner of delivery; and, multifaceted interventions are often more effective than ones consisting of a single activity. CONCLUSIONS: This scoping review identifies occupational disease prevention strategies worthy of further exploration by decisionmakers and stakeholders and of future systematic evaluation by researchers. It also identified important gaps, including a lack of studies of precarious workers and the need for more studies that rigorously evaluate the effectiveness of interventions.
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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 | 0.033 | 0.122 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.040 | 0.029 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".