Screening for hand dermatitis in healthcare workers: Comparing workplace screening with dermatologist photo screening
Bibliographic record
Abstract
BACKGROUND: Healthcare workers are at increased risk for occupational contact dermatitis, owing to wet work exposure. Early detection and management improves outcomes. Although several diagnostic tools are available, none is appropriate for rapid screening. OBJECTIVES: To assess the validity and feasibility of the Hand Dermatitis Screening Tool in the acute healthcare sector. METHODS: Screening of 508 employees at three hospitals in Ontario, Canada was performed with the Hand Dermatitis Screening Tool either by an occupational health nurse (N = 225) or by self-administration (N = 283). Two occupational dermatologists rated photographs of participants' hands. RESULTS: Of the participants, 30.5% screened positive for hand dermatitis. A positive screen was associated with wet work, history of eczema, dermatitis, or other rash, and currently having a rash. Ninety-four per cent of participants reported that using the tool took <2 minutes, 99% indicated that the tool was easy to use, and 86% stated that workplace screening was very important. Workplace and dermatologist photo screening showed fair agreement. CONCLUSIONS: The prevalence of hand dermatitis and identified risk factors were consistent with the literature. These findings, along with positive feasibility results, support further testing of the tool despite only fair agreement between workplace and dermatologist screening.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".