P-341 Sun exposure in outdoor workers: key considerations for an occupational surveillance system
Bibliographic record
Abstract
Introduction Outdoor workers are exposed to a variety of hazards, including solar ultraviolet radiation (UVR). Identifying, reporting, analyzing, and tracking the exposures or health outcomes of outdoor workers specifically have not generally been considered in a formalized way. Objectives Our objective was to identify the key characteristics and the barriers and facilitators of occupational surveillance systems in order to make recommendations for a system for outdoor workers that includes consideration of sun exposure and skin cancer. Methods A traditional literature review (peer-reviewed and grey literature) was performed using search terms relevant for surveillance, outdoor workers, and best practices. Additionally, 22 qualitative key informant interviews were conducted with a variety of experts. The audio recorded interviews were transcribed verbatim and coded for broad themes and specific barriers and facilitators using NVivo 12. Results The literature review found no occupational surveillance programs focused solely on outdoor workers. Five occupational surveillance strategies were summarized to obtain a better understanding of occupational surveillance systems and how they might be applied to keratinocyte carcinoma (KC) or solar UVR exposure in outdoor workers. The key informant interviews revealed ten key considerations for the design of a surveillance system, including identifying a clear goal, a defined target population and stakeholder involvement. Additionally, five critical barriers including underreporting and funding, and five vital facilitators including communication/collaboration and a simple reporting process were identified. Conclusion Our study demonstrated that barriers and facilitators to an occupational surveillance system for outdoor workers exist and thoughtful design and implementation are key. Some specific suggestions for a successful occupational surveillance program for outdoor workers include the recognition of KC as an occupational disease, designing and implementing a notification/data collection mechanism for KC, continuing to build primary prevention initiatives, educating workers/employers on the risks of skin cancer and other outdoor hazards, and investing long-term into surveillance.
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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 | 0.050 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".