Outdoor Workers' Use of Sun Protection at Work and Leisure
Bibliographic record
Abstract
BACKGROUND: Outdoor workers are at risk of high ultraviolet radiation exposure, and may have difficulty using sun protection. The objectives were to determine the prevalence of sun protection behaviors in a sample of outdoor construction workers, and to assess which factors predict better sun protection practices. METHODS: Participants were recruited via construction unions. Workers answered a questionnaire on demographics, skin cancer risk, sun protection behaviors, and job. Sun protection behavior scores (from questions on sunscreen use, sleeved shirt, hat, shade seeking, sunglasses) were calculated by converting Likert-scale answers to scores from 0 to 4, and taking the mean (separately for work and leisure). Determinants of sun protection behavior scores were examined for work and leisure using generalized linear models. RESULTS: Seventy-seven workers had complete questionnaire data (participation 98%). Sun protection behaviors used most often were hats (79% often/always) and sleeved shirts (82% often/always); least prevalent were shade-seeking (8% often/always) and sunscreen (29% often/always). For both work and leisure scores, the strongest predictor was skin type, with fairer-skinned individuals having higher sun protection behavior scores. Workers had higher scores at work than on weekends. Workplaces that required hats and sleeved shirts for safety purposes had higher protection behavior scores. CONCLUSION: This high-participation rate cohort helps characterize sun protection behaviors among outdoor workers. Workers practiced better sun protection at work than on weekends, suggesting that workplace policies supportive of sun protection could be useful for skin cancer prevention in the construction industry.
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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 | 0.000 | 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 | 0.000 | 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) | 0.004 | 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 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".