State of the Art and Challenges for Occupational Health and Safety Performance Evaluation Tools
Bibliographic record
Abstract
In industrialized nations, occupational health and safety (OHS) has been a growing concern in many businesses for at least two decades. Legislation, regulation, and standards have been developed in order to provide organizations with a framework for practicing accident and illness prevention and placing worker well-being at the center of production system design. However, the occurrence of several accidents continues to show that OHS performance evaluation is subject to interpretation. In this review of the literature, we outline the scope of current research on OHS status and performance evaluation and comment on the suitability of the instruments being proposed for field use. This study is based on a keyword-based bibliographical search in the largest scientific databases and OHS-related websites, which allowed us to identify 15 OHS performance evaluation tools. Our principal conclusion is that researchers in the field have shown little interest in generalizing the instruments of OHS performance evaluation and that none of the 15 tools examined is properly applicable to any real organization outside of the sector of activity, economic scale, and jurisdiction for which it was designed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".