Occupational Hand Dermatitis in Health Care: Development and Evaluation of an Online Training E-Module
Bibliographic record
Abstract
Background: Occupational hand dermatitis (OHD) is an important health concern for health care workers (HCWs), yet there is a lack of accessible training materials on this topic. Objectives: The objective of this study was to develop and evaluate an OHD training e-module for HCWs. Methods: The e-module was created in collaboration with an expert advisory committee and tested by Ontario HCWs through pre- and post-training OHD knowledge tests, a usability survey, and a survey about intent to change work skin care practices. Analyses of survey results included means and paired t -tests. Results: The 10-minute OHD training e-module for HCWs was tested by 254 HCWs and found to be highly usable, to increase OHD knowledge immediately and sustainably, and to change workplace skin care practices. Average OHD knowledge test scores significantly improved by 19% between the pretest (64.50%) and post-test (83.50%). Most 6-month follow-up survey respondents reported changing their skin care work practices (76.69%). Conclusions: This research addresses the previous lack of accessible OHD training for workers in health care settings. The creation and evaluation of a no-cost accessible OHD training e-module for workers in health care settings showed promising results across knowledge increase, knowledge retention, skin care behavior changes, and usability.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".