P-407 CALCULATING DATA FOR NATIONAL OCCUPATIONAL HEALTH AND SAFETY NATIONAL PROFILES IN ACCORDANCE WITH ILO RECOMMENDATION 197
Bibliographic record
Abstract
Abstract Introduction Developing national occupational health and safety (OHS) profiles involves data collection and analysis to illuminate a country’s workplace safety landscape. This abstract explores data calculation methodologies for comprehensive national OHS profiles while adhering to the International Labour Organization (ILO) Recommendation 197. Methodology The data calculation process for national OHS profiles involves gathering information from diverse sources, including government records, industry reports, surveys, and health databases. This data encompasses workplace injury rates, hazard exposure, safety regulation compliance, and workforce demographics, aligning with ILO Recommendation 197. Results Analyzed data identifies trends, patterns, and disparities in OHS performance across sectors and regions, in line with the spirit of Recommendation 197. Key metrics such as injury frequency and severity rates quantitatively represent workplace safety challenges, ensuring they adhere to ILO guidelines. Discussion Adhering to the principles of Recommendation 197 is vital for meaningful national OHS profiles. Challenges like underreporting and data collection variations impact accuracy. Standardizing data collection methods and improving reporting practices, in accordance with ILO recommendations, enhances data precision. Conclusion The calculation of data for national OHS profiles requires a systematic and holistic approach. Adhering to ILO Recommendation 197 enhances the reliability and comparability of the collected data. As stakeholders strive to create standardized methodologies for data calculation and reporting, in alignment with international guidelines, they contribute to a safer and healthier work environment on a national level. The application of Recommendation 197 ensures that national OHS profiles not only provide accurate insights but also resonate with globally recognized best practices.
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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 | 0.155 | 0.450 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.041 | 0.065 |
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".