0382 Increasing male/female inequalities in rates of workplace violence in ontario between 2002 and 2014: a comparison of two data sources
Bibliographic record
Abstract
Literature examining male/female differences in rates of workplace violence has produced mixed findings. This study examined trends in rates of workplace violence using two population level data sources. These were: workers’ compensation claims for assaults that required time off work; and emergency department visits for assaults or accidental contact from another person, where the treating physician determined that the payer should be workers’ compensation. For both data sources, denominator information of the population at risk was generated by sex, age groups and time period using the Labour Force Survey. Over the period 2002 to 2014 rates of assault among men remained stable, from 31.5 per 100,000 FTEs to 32.5 per 100,000 FTEs. Conversely among women rates of lost-time claims due to workplace violence increased from 38.9 per 100,000 FTEs to 59.1 per 100,000 FTEs - an absolute increase of 20.2 assaults per 100,000 FTEs, and a relative increase of 52%. These divergent trends were mirrored in the emergency department records, with rates of ED presentations among men remaining stable between 2004 and 2014 (38.2 to 39.8 per 100,000 FTEs); while among women rates of presentation increased from 34.9 per 100,000 FTEs to 52.9 per 100,000 FTEs - a relative increase of over 50%. In both time periods rates of assaults were relatively stable for men and women up till about 2008/09, after which point rates diverged between men and women. Using two data sources this study demonstrates increasing male/female inequalities in workplace violence in Ontario.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".