Higher unemployment and higher work-related traumatic fatality: trends and associations from the Canadian province of Saskatchewan, 2007–2018
Bibliographic record
Abstract
OBJECTIVE: Although Saskatchewan appears to have the greatest burden of work-related fatality (WRF) in Canada, it is unclear how WRF rates have varied over time. We investigated the WRF rate in Saskatchewan over the past decade and modeled potential risk factors for WRF, including economic indicators. METHODS: In this cross-sectional, population-based study, Saskatchewan workplace traumatic fatalities grouped by year, season, and worker characteristics (eg, age, industry) were used in addition to Statistics Canada labor force survey total employment, total labor force, and the number of unemployed workers by year and season. WRF rates were calculated as fatalities per total number of employed workers. A Poisson generalized additive model was employed to examine the association between WRF rates and personal characteristics, and economic indicators. RESULTS: The rate remained fairly stable between 2013-2014 and 2015-2017 but sharply increased from 2017 to 2018. On average, the highest rate was observed among workers aged ≥60 years (0.70 ± 0.21 per 100 000). Men had a more than 13-fold greater risk of WRF than women [relative risk (RR)13.7, 95% confidence interval (CI) 10.48-17.9), with the highest RR of WRF observed in the construction industry (RR 9.2, 95% CI 6.1-13.8). The risk of mortality increased non-linearly with increasing unemployment rate, with instability as the unemployment rate reaches the highest modeled values. CONCLUSION: Workplace fatality in the province has fluctuated over the past decade, with differential impact observed among industry groups. Furthermore, an increase in the unemployment rate was followed by an increase in mortality risk. Prioritizing and encouraging prevention strategies during periods of economic recessions could help address the incidence of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".