Developing a tool for identifying high-risk employers for inspection
Bibliographic record
Abstract
BACKGROUND: Workers' Compensation Board (WCB) data and other information are sometimes used to calculate an 'Occupational Health and Safety (OHS) index' as a way of identifying businesses considered 'high risk' to be inspected as part of enforcement work. However, no evidence on the validity of this index exists. AIMS: To evaluate the performance of the Alberta OHS index, a 'score' based largely on WCB claims data, and to see if an index calculated using different information could perform better. METHODS: Data from the Alberta Compliance Management Information System database, 2011-2015, and WCB claim database, 2007-2014, were retrieved. Issuing 'stop work' or 'stop use' orders in inspections was defined as a proxy of high-risk outcome. The performance of the current and a modified OHS index were assessed using receiver operating characteristics (ROC) and regression analyses. RESULTS: In large employers, neither the current nor the modified OHS index was particularly effective in identifying 'high risk' employers with the area under the ROC curve (AROC) of 0.55 (95% confidence interval [CI] 0.52-0.57; P < 0.001) and 0.59 (95% CI 0.57-0.62; P < 0.001), respectively. In small employers, neither index seemed very effective with an AROC of 0.54 (95% CI 0.53-0.56; P < 0.001) and 0.55 (95% CI 0.53-0.56; P < 0.001), respectively. These results were consistent in subgroup analyses of assignments without specific initiatives, both in large and small employers. CONCLUSIONS: Neither the current nor a modified OHS index seemed to effectively identify high-risk employers. Heterogeneous results in large and small employers suggest that approaches to different-sized employers are appropriate.
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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 | 0.025 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".