Awareness of occupational skin disease in the service sector
Bibliographic record
Abstract
BACKGROUND: Occupational skin disease (OSD) is a common occupational disease. Although primary prevention strategies are known, OSDs remain prevalent in a variety of work environments including the service sector (restaurant/food services, retail/wholesale, tourism/hospitality and vehicle sales and service). AIMS: To obtain information about awareness and prevention of OSD in the service sector. METHODS: Focus groups and a survey were conducted with two groups. The first consisted of staff of the provincial health and safety association for the service sector and the second group comprised representatives from sector employers. Focus groups highlighted key issues to inform the survey that obtained information about perceptions of awareness and prevention of OSD and barriers to awareness and prevention. RESULTS: Both provincial health and safety association staff and sector employer representatives highlighted low awareness and a low level of knowledge of OSD in the sector. Barriers to awareness and prevention included a low reported incidence of OSD, low priority, lack of training materials, lack of time and cost of training, lack of management support and workplace culture. CONCLUSIONS: A starting point for improving prevention of OSD in the service sector is increased awareness. Identification of the barriers to awareness and prevention will help to shape an awareness campaign and prevention strategies. Building on existing experience in Europe will be important.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".