Chemical Risk Assessment for Small Businesses: Development of the Chemical Hazard Assessment and Prioritization Risk (CHAP-Risk) Tool
Bibliographic record
Abstract
There are a large number of chemicals commercially available, but relatively few have legislated occupational exposure limits. Assessing the hazard and risk posed by most chemicals used in the workplace is therefore challenging, especially for small workplaces. This paper describes the development of an easy-to-use MS Excel spreadsheet-based tool (called CHAP-Risk) designed to assist small businesses to undertake a simple assessment of the health and safety risks posed by the chemicals they use. We developed the CHAP-Risk tool through engaging an expert review panel and undertaking a detailed review of existing tools, and by validating a trial version which was piloted by six workplaces and 59 workers. We received multiple rounds of feedback from key experts and end-users, and in response, through 58 versions, refined CHAP-Risk to produce the final free public-release version of the tool. Workplace participants thought that the CHAP-Risk tool would be useful in improving worker and employers’ understanding of workplace chemical risks. However, because this tool required users to have more in-depth knowledge of workplaces’ processes, there was mixed feedback on its usability: those with OHS training were very positive, while others thought it would be too difficult for shop-floor workers to use.
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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 | 0.020 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".