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.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 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".