P-398 Chemical risk assessment for small business: development of a tool and resources
Bibliographic record
Abstract
<h3>Introduction</h3> Small to medium sized enterprises (SMEs) employ the majority of Canadians, but find it difficult to assess the risks of the chemicals they use and to implement effective control and prevention measures. As such, there is an ongoing need to provide SMEs with easy-to-use tools to identify chemical hazards and assess the risks posed by exposure to chemical agents. <h3>Objective</h3> To develop CHAP-Risk, a software tool to assist SMEs to easily undertake health and safety risk assessments of the chemicals/hazardous products they use, and assist them to implement effective control measures. <h3>Methods</h3> We have employed a ‘User Centered Design’ approach which allows end-users to influence how the design takes shape. Our eight step development process is: (1) establish a project reference group, (2a) develop a trial version of CHAP-Risk and its training program based on an extensive review of existing tools and resources, (2b) recruit trial workplaces – 6 workplaces in Ontario were recruited to trial the tool, (3) pre-trail evaluation, (4) training, (5) workplace trial, (6) post-trial evaluation, (7) develop final versions of CHAP-Risk and the training, and (8) CHAP-risk and resources available on website for free download (by September 2021). <h3>Results</h3> The draft version of CHAP-Risk is currently being trialed by workplaces (trial to be completed in June/July 2021). Initial feedback is positive in regard to the potential benefits and application of the tool, particularly related to improving the ‘workplace contextualization’ of the WHMIS 2015 requirements. However, a number of technical improvements are currently needed to enhance the user experience. <h3>Conclusion</h3> Tools to support SMEs in undertaking chemical risk assessments are important and the current trial of CHAP-Risk indicates that it will provide SMEs with a useful tool to assist them in assessing and more safely managing their workplace chemicals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".