Air temperatures and occupational injuries in the agricultural settings: a report from Northern Italy (Po River Valley, 2013-2017).
Bibliographic record
Abstract
INTRODUCTION: High environmental temperatures are associated with an increased risk for occupational injuries (OIs), particularly where environmental exposure and heat sources in the workplace, are associated with internal heat generation by strenuous muscular work. As a consequence, Agricultural Workers (AWs) are among the most heavily affected occupational groups. METHODS AND AIMS: The aim of this study was to assess the relationship between environmental temperatures and OIs in AWs from the Po River Valley in the Northern Italy (27,736,158 total inhabitants; mean agricultural workforce of 312,195.6 individuals). Data about OIs from 2013 to 2017, and daily weather for the administrative unit of occurrence were retrieved. Days were classified by a) mininum (Tmin) / maximum (Tmax) air temperatures; b) average day temperature (Tday); c) daily temperature variation (TV), d) relative humidity. Risk for daily OIs was calculated as correspondent Odds Ratios (OR) through a Poisson regression model. RESULTS: Estimated incidence for OIs was 66.3/1,000 workers-year. In regression analysis, for every Tday percentile increase equal to 2.5, an OR 1.007 (95% CI, 1.003 to 1.010) was reported. More precisely, higher risk for OIs was associated to Tmax > 25°C (OR 1.143, 95%CI 1.125-1.160) and to Tmax > 25°C + Tmin > 20°C (OR 1.158, 95%CI 1.138-1.179), Tmin < 0°C were associated with a significantly reduced risk (OR 0.879, 95%CI 0.850-0.910), with the notable exception of older age groups (OR 1.348, 95%CI 1.254; 1.449). During timeframes characterized by Tmax > 35°C (i.e. HW time period), the risk was higher during the first day (OR 1.266; 95%CI 1.206-1.330), and again from the fourth day onwards (OR 1.090; 95%CI 1.048 - 1.133). Analysis of TV identified an increased risk for occupational injuries in days characterized by higher variability, and particularly for TV ranging 4.0 - 4.9 (OR 1.042, 95%CI 1.017 - 1.068), and equals to 5.0 or greater (OR 1.143, 95%CI 1.118 - 1.167). Also increased relative humidity was associated with higher risk for OIs (OR 1.096, 95%CI 1.081-1.126, and OR 1.154, 95%CI 1.135-1.173 for relative humidity 70 - 89%, and ≥ 90%). CONCLUSIONS: Our findings recommend policymakers to develop appropriate procedures and guidelines, in particular for the HW time periods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".