Skin-specific training experience of workers assessed for contact dermatitis
Bibliographic record
Abstract
Background: Contact dermatitis is a common and preventable work-related disease. Skin-specific training may be effective for preventing occupational contact dermatitis, but little information is available regarding actual workplace training and its effectiveness. Aims: To describe workplace skin-specific training among workers with suspected contact dermatitis. Methods: Patch test patients being assessed for suspected contact dermatitis at an occupational health clinic in Toronto, Canada, completed a questionnaire on training experiences, workplace characteristics, exposures and skin protection practices. Results: Of 175 patients approached, 122 (71%) workers completed questionnaires. Many (80%) had received general occupational health and safety and hazardous materials training (76%). Fewer (39%) received skin-specific training. Of those with work-related contact dermatitis, 52% did not receive skin-specific training. Skin-specific training was commonly provided by health and safety professionals or supervisors using video, classroom and online techniques. Content included glove use, exposure avoidance and hand washing information. Workers that received skin-specific training found it memorable (87%), useful (85%) and common sense in nature (100%). Conclusions: This study indicates gaps in workplace training on skin disease prevention for workers with contact dermatitis. Workers perceived skin-specific training to be useful. Understanding worker training experiences is important to prevention programme development and reducing work-related skin disease.
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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 | 0.002 |
| 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 | 0.000 | 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".