Bibliographic record
Abstract
Abstract Global estimates of the work-related burden of disease and injury indicate that each year 1.9 million people die from exposure to occupational risk factors, 81% of these fatalities resulting from non-communicable, occupational diseases. These are underestimates of the burden of occupational exposure and disease. We are not effectively preventing occupational disease and related fatalities through the classical approaches to occupational safety and health and risk management. There is an urgent need for alternate strategies to prevent occupational illness. Total Worker Health® (TWH®), introduced by NIOSH in 2011, offers an approach for consideration that could serve as a model across geographies and sectors to improve worker wellbeing, mitigation of risk, and ultimately, prevention of occupational disease and related fatality. This presentation will include the recent global estimates of non-communicable, occupational disease published by the World Health Organization and International Labour Office in 2021, and a review of the International Council on Mining and Metals’ Critical Control Management (CCM) process. TWH® will be defined and issues related to advancing worker wellbeing will be introduced. The other “Totals” and their relationships to TWH will be explained. The presentation will highlight the challenges in applying the CCM approach to critical risks associated with overexposure to chronic occupational health hazards such as silica and other carcinogens. In these intersecting topic areas, occupational hygienists and other OEHS professionals as exposure scientists can have meaningful input into prevention strategies and programs and improved worker health outcomes. Collaborative opportunities offering sustainable solutions will be introduced and discussed.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".