Construction sector joint health and safety committees: Can their effectiveness be assessed using the JHSC assessment eTool?
Bibliographic record
Abstract
BACKGROUND: Joint health and safety committees (JHSCs) are a legal requirement for most Ontario workplaces. However, few evidence-based tools exist to assess JHSC effectiveness. The JHSC Assessment eTool has been developed and tested in the healthcare and education sectors with positive results. OBJECTIVE: The objective of this study is to test the usability and feasibility of the JHSC Assessment eTool in the construction sector. METHODS: Testing was carried out in two phases using a non-experimental study design. In the first phase, construction sector health and safety experts reviewed the content of the eTool to ensure that it had relevance for the sector. In phase two, a convenience sample of JHSCs from construction sector workplaces were recruited to pilot the eTool and provide feedback. RESULTS: Feedback from six constructor sector health and safety experts and six JHSC committees indicated that the eTool had value and relevance for use in the sector. Additionally, our study identified several minor areas for refinement including the addition of construction-specific resources in the final report. CONCLUSIONS: The results of our study suggest minor modifications to the eTool for use by construction sector workplaces.
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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 | 0.133 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".