Associations between temporary employment and occupational injury: what are the mechanisms?
Bibliographic record
Abstract
OBJECTIVE: To determine whether observed higher risks of occupational injury among temporary workers are due to exposure to hazardous working conditions and/or to lack of job experience level. METHODS: Data systematically recorded for 2000 and 2001 by the Spanish Ministry of Labour and Social Affairs on fatal and non-fatal traumatic occupational injuries were examined by type of employment and type of accident, while adjusting for gender, age, occupation, and length of employment in the company. In the study period there were 1500 fatal and 1 806 532 non-fatal traumatic occupational injuries that occurred at the workplace. Incidence rates and rate ratios (RR) were estimated using Poisson regression models. RESULTS: Temporary workers showed a rate ratio of 2.94 for non-fatal occupational injuries (95% CI 2.40 to 3.61) and 2.54 for fatal occupational injuries (95% CI 1.88 to 3.42). When these associations were adjusted by gender, age, occupation, and especially length of employment, they loose statistic significance: 1.05 (95% CI 0.97 to 1.12) for non-fatal and 1.07 (95% CI 0.91 to 1.26) for fatal. CONCLUSIONS: Lower job experience and knowledge of workplace hazards, measured by length of employment, is a possible mechanism to explain the consistent association between temporary workers and occupational injury. The role of working conditions associated with temporary jobs should be assessed more specifically.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".