Beyond the Clock: Revisiting Occupational Exposure Limits (OELs) for unusual work schedules in the South African Mining Industry
Bibliographic record
Abstract
Workers are exposed to various stressors in their workplace and in many industries, exposure is prolonged due to unusual work schedules. Unfortunately, current Occupational Exposure Limits (OELs) are derived from calculations based on a conventional work schedule of five consecutive, eight-hour days (40 h per week) and do not compensate for prolonged exposure and shortened recovery time. Therefore, standard OELs must be adjusted to ensure worker protection. The aim of this paper was to review different mathematical models available in the literature to adjust OELs for unusual work schedules. Based on the advantages, disadvantages and practical feasibility of the different models, the Brief and Scala, Occupational Safety and Health Administration (OSHA) and Québec models were selected to calculate reduction factors (RFs, i.e., adjustment factors) for three unusual work schedules encountered in the South African Mining Industry (SAMI). Subsequently, the calculated RFs were used to establish adjusted OELs (OELA) for 15 chemical substances of interest. The Brief and Scala model yielded the most conservative RFs for all work schedule examples. However, this model may overestimate the degree to which the OELs should be lowered. The OSHA and Québec models incorporate pharmacokinetic parameters (i.e., primary health effects) and generated more realistic RFs compared to the Brief and Scala model. Although pharmacokinetic-based models are more accurate from a toxicological viewpoint, the anatomical, physiological, biochemical, and physiochemical parameters required to apply these models are not available for many chemical substances and therefore the use of such pharmacokinetic-based models is not practically feasible in South Africa. Based on the findings of this study, the authors recommend using the OSHA or Québec models for OEL adjustments in the SAMI. OELs adjusted in this manner will provide an equivalent degree of worker protection during unusual work schedules compared to conventional work shifts.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".