Tailored Sun Safety Messages for Outdoor Workers
Bibliographic record
Abstract
Background: Messaging surrounding skin cancer prevention has previously focused on the general public and emphasized how or when activities should be undertaken to reduce solar ultraviolet radiation (UVR) exposure. Generic messages may not be applicable to all settings, and should be tailored to protect unique and/or highly susceptible subpopulations, such as outdoor workers. The primary objective of this study was to develop a set of tailored, practical, harm-reducing sun safety messages that will better support outdoor workers and their employers in reducing the risk of solar UVR exposure and UVR-related occupational illnesses. Methods: We adapted a core set of sun safety messages previously developed for the general population to be more applicable and actionable by outdoor workers and their employers. This study used an integrated knowledge translation approach and a modified Delphi method (which uses a survey-based consensus process) to tailor the established set of sun safety messages for use for outdoor worker populations. Results: The tailored messages were created with a consideration for what is feasible for outdoor workers, and provide users with key facts, recommendations, and tips related to preventing skin cancer, eye damage, and heat stress, specifically when working outdoors. Conclusion: The resulting tailored messages are a set of evidence-based, expert- approved, and stakeholder-workshopped messages that can be used in a variety of work settings as part of an exposure control plan for employers with outdoor workers.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".