Sun Safety at Work Canada: a multiple case-study protocol to develop sun safety and heat protection programs and policies for outdoor workers
Bibliographic record
Abstract
BACKGROUND: CAREX Canada has identified solar ultraviolet radiation (UV) as the second most prominent carcinogenic exposure in Canada, and over 75 % of Canadian outdoor workers fall within the highest exposure category. Heat stress also presents an important public health issue, particularly for outdoor workers. The most serious form of heat stress is heat stroke, which can cause irreversible damage to the heart, lungs, kidneys, and liver. Although the need for sun and heat protection has been identified, there is no Canada-wide heat and sun safety program for outdoor workers. Further, no prevention programs have addressed both skin cancer prevention and heat stress in an integrated approach. The aim of this partnered study is to evaluate whether a multi-implementation, multi-evaluation approach can help develop sustainable workplace-specific programs, policies, and procedures to increase the use of UV safety and heat protection. METHODS/DESIGN: This 2-year study is a theory-driven, multi-site, non-randomized study design with a cross-case analysis of 13 workplaces across four provinces in Canada. The first phase of the study includes the development of workplace-specific programs with the support of the intensive engagement of knowledge brokers. There will be a three-points-in-time evaluation with process and impact components involving the occupational health and safety (OHS) director, management, and workers with the goal of measuring changes in workplace policies, procedures, and practices. It will use mixed methods involving semi-structured key informant interviews, focus groups, surveys, site observations, and UV dosimetry assessment. Using the findings from phase I, in phase 2, a web-based, interactive, intervention planning tool for workplaces will be developed, as will the intensive engagement of intermediaries such as industry decision-makers to link to policymakers about the importance of heat and sun safety for outdoor workers. DISCUSSION: Solar UV and heat are both health and safety hazards. Using an occupational health and safety risk assessment and control framework, Sun Safety at Work Canada will support workplaces to assess their exposure risks, implement control strategies that build on their existing programs, and embed the controls into their existing occupational health and safety system.
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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 | 0.056 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".