A case control study of differences in non-work injury and accidents among sawmill workers in rural compared to urban British Columbia, Canada
Bibliographic record
Abstract
Abstract Background Using a cohort of British Columbian male sawmill workers, we conducted a nested case-control study of the impact of rural compared to urban residence as well as rural/urban migration patterns in relation to hospitalization for non-work injury. We postulate that for many types of non-work injuries, rates will be higher in rural communities than in urban ones and that rates will also be higher for workers who migrate from urban to rural communities. Methods Using conditional logistic regression, univariate models were first run with each of five non-work injury outcomes. These outcomes were hospitalizations due to assault, accidental poisoning, medical mis-adventure, motor vehicle trauma, and other non-work injuries. In multivariate models marital status, ethnicity, duration of employment, and occupation were forced into the model and associations with urban, compared to rural, residence and various urban/migration patterns were tested. Results Urban or rural residence and migration status from urban to other communities, and across rural communities, were not associated with hospitalization for medical misadventure, assault, or accidental poisoning. The likelihood of a rural resident being hospitalized for motor vehicle trauma is higher than for an urban resident. The likelihood that a rural resident is hospitalized for "other" non-work injury is higher than for an urban resident. Conclusion In a relatively homogenous group of workers, and using a rigorous study design, we have demonstrated that the odds of other non-work injury are much higher for workers resident in and migrating to rural regions of Canada than they are for workers resident in or migrating to urban places.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".