A quantitative solar ultraviolet radiation job-exposure matrix for Europe
Bibliographic record
Abstract
INTRODUCTION: Outdoor workers are exposed to high levels of solar ultraviolet radiation (UVR). UVR causes skin cancer and is a risk factor for cataract and other short- and long-term health effects, but there are significant knowledge gaps regarding the exposure-response relations based on quantitative measures of UVR exposure. We developed a quantitative UVR job-exposure matrix (JEM) for the general working population of Europe. METHODS: Three experts from each of Northern, Central, and Southern Europe rated duration of outdoor work for all 372 occupations defined by the International Standard Classification of Occupations from 1988 (ISCO-88(COM)). A systematic literature search identified 12 studies providing 223 sets of summary workday UVR exposure for 49 ISCO-88(COM) occupations based on 75,711 personal workday measurements obtained from 2,645 participants and reported as arithmetic mean standard erythemal dose (SED). We combined the expert ratings with the measured occupational UVR exposure data and estimated harmonized workday UVR exposures for all 372 occupations in a linear mixed effects model. RESULTS: Monotonically increasing workday UVR exposure of 0.68, 1.57, 1.80, and 2.49 SED were seen by increasing expert ratings of 0, 1 to 2, 3 to 4, and ≥5 h of daily outdoor work. The UVR exposure showed a 6-fold increase from lowest to highest exposed occupation. Farm hands, roofers, concrete placers, and other occupations within craft and related trades were among the highest exposed, while bartenders, wood-processing-plant operators, and several white-collar occupations who typically work indoor were among the lowest exposed. CONCLUSION: This quantitative JEM for solar UVR exposure proves able to provide substantial discrimination between occupations, shows good agreement with expert assessments, and may facilitate epidemiological studies characterizing the exposure-response relation between occupational solar UVR exposure and different health effects.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".