Strategic Task and Break Timing to Reduce Ultraviolet Radiation Exposure in Outdoor Workers
Bibliographic record
Abstract
Objectives Public health messaging about sun avoidance strategies is often not practical for outdoor workers. The objective of this study was to use personal monitoring data to determine when peak UVR exposure occurs for outdoor workers, estimate how much UVR could be reduced by altering the timing of shady tasks or breaks during peak exposure times, and descriptively compare these to peak periods of ambient UVR. Ultimately, we aim to provide evidence-based sun avoidance recommendations for outdoor workers in British Columbia, Canada. Methods UVR exposure data (standard erythemal dose (SED)) were collected during the 2013 summer months in Vancouver, using personal electronic dosimeters that sampled once per minute for an average of 4.4 working days (range: 1 to 7 days). Mixed-effect models were used to estimate the 60-, 30-, and 15-minute time intervals at which maximum exposure occurred for the months of July and August. Using these time intervals, UVR exposure during peak periods was summarized as SED and as a percentage of the total daily exposure. Ambient UVR was also collected using data from the nearest Brewer spectrophotometer station and parallel analyses were conducted. Results There were 73 workers and 321 participant-days available for analysis. Models indicated that periods of maximum exposure for 15-, 30-, and 60-minute intervals began at 12:28pm, 12:17pm, and 11:52am, respectively, for sunny days in July. These periods were similar in August. The median exposure during these time periods and the potential for reducing UVR was 0.03 SED (2.8% potential daily exposure reduction), 0.09 SED (7.1%), and 0.18 SED (15.9%), respectively. However, there was a large range in exposure estimates as some workers experienced up to 84.8% of their exposure in the peak 60-minute interval. Conclusion Skin cancer prevention messaging does not include practical messages for outdoor workers and providing times of peak UVR help to identify times when the greatest reductions in exposure can occur. Prevention measures including shady breaks, increased sun protection, and task reorganization during these peak times are recommended during these peak times to reduce UVR exposure among those at highest risk.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".