Usage of gloves for hair shampooing in German hairdressing salons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Occupational skin disease caused by wet work is particularly common in employees in hairdressing salons. The objective of this paper was to determine the frequency of glove use for hair shampooing. METHODS: Data on the usage of gloves for hair shampooing were collected by covert observations in four cross-sectional surveys of newly opened hairdressing salons located in Cologne. Measurements were conducted between 2009 and 2012. A team of five trained observers were involved in the measurements. As a second assessment method, salon owners of other newly opened salons from five districts of Germany were interviewed by telephone at three of the four measurement points. Trend analysis was performed with the Mantel-Haenszel test for trends and simple linear regression. Differences in proportions of glove use between the two assessment methods were compared by chi-squared tests. RESULTS: In total, 435 hair shampoos were observed and 630 salon owners interviewed. Gloves were worn in 14 % of the observed hair shampoos. Proportions of glove use differed significantly according to assessment measurement. Proportions of glove use assessed by covert observation increased from 10.5 % to 18.5 % (ptrend = 0.044) whereas the proportions obtained by telephone interview decreased over the study period from 84 % to 76 % (ptrend = 0.037). No trend was found for the intensity of glove use (ptrend = 0.204). CONCLUSIONS: Gloves were worn in less than 20 % of hair shampoos. This rate is much lower than values reported from other written or verbal surveys. Future measures for skin protection in hairdressing salons should take this into account.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it