Cutaneous and respiratory symptoms among professional cleaners
Bibliographic record
Abstract
BACKGROUND: Occupational dermatitis is very common and has a large economic impact. Cleaners are at an increased risk for both work-related cutaneous and respiratory symptoms. AIMS: To compare the prevalence of occupational cutaneous symptoms among professional indoor cleaners to other building workers (OBW) and to determine associations with exposures and with respiratory symptoms among cleaners. METHODS: A questionnaire completed by indoor professional cleaners and OBW to compare rash and respiratory symptoms between these groups examined workplace factors such as training, protective equipment and work tasks. RESULTS: In total, 549 of the 1396 professional cleaners (39%) and 593 of the 1271 OBW (47%) completed questionnaires. The prevalence of rash was significantly higher in the cleaners compared to the OBW. For male cleaners, 21% (86/413) had a rash in the past 12 months compared to only 11% (13/115) of OBW (P < 0.05). The rashes experienced by the cleaners were more likely to be on their hands and worse at work. Cleaners washed their hands significantly more often than OBW. Cleaners with a rash were less likely to have received workplace training regarding their skin and were more likely to find the safety training hard to understand. Cleaners with a rash within the past year were significantly more likely to have work-related asthma symptoms than cleaners without a rash (P < 0.001). CONCLUSIONS: This study demonstrates a strong link between work-related symptoms of asthma and dermatitis among cleaners. Effective preventive measures, such as the use of protective skin and respiratory equipment, should be emphasized.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".