P-77 Work-related traumatic fatality in the Canadian province of Saskatchewan, 2007–2018: trends and association with economic factors
Bibliographic record
Abstract
Background Understanding the extent of work-related fatality (WRF) burden can provide insight into prevention efforts. The Canadian province of Saskatchewan demonstrates an increased WRF burden over other provinces. Still, the evolution of this WRF burden over time remains unclear and this limits understanding of the true pattern of fatalities at the workplace and identification of potential WRF leading indicators. This study examined the WRF rate in Saskatchewan over the past decade, as well as potential risk factors. Methods Retrospective linked Saskatchewan workplace traumatic fatalities and Statistics Canada labour force survey data were used. Fatality cases were then aggregated by year, season, worker characteristics (e.g., age, sex, and industry type), total employment, total labour force, and the number of unemployed workers. Yearly WRF rates were calculated using the number of fatalities as the numerator and yearly total employment numbers as the denominator. A generalized additive model with Poisson distribution was carried out to examine the association of WRF rates to personal characteristics and economic indicators. Results The study identified 220 traumatic WRF cases from 2007 to 2018. The average twelve-year WRF rate was 0.28 ± 0.07 per 100,000, with a stable WRF rate observed between 2013–2014 and 2015–2017 and an increasing trend between 2017–2018. Men were 13 times more likely to have WRF than women (RR=13.7, 95% CI: 10.48–17.9), and participants aged 60+ years were disproportionately affected by WRF (0.70 ± 0.21 per 100,000). The construction industry experienced the highest WRF risk (RR=9.2, 95%CI 6.1–13.8). Risk of WRF was found to increase with unemployment rate, but dropped when unemployment rate was highest. Conclusion The study findings show a rising trend in recent (2017–2018) WRF rate, with transient increases in unemployment rate compounding the problem. Targeting prevention strategies towards high-risk population and age groups and during periods of economic downturn could help address fatalities at work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".