0418 Violence in healthcare: how does it affect return-to-work after work injury?
Bibliographic record
Abstract
Objectives Research suggests an association between violence towards healthcare workers and poor return-to-work (RTW) outcomes. This association may be due to healthcare specific factors such as care setting and injury type. The aim of the study is to investigate RTW outcomes after injuries due to violence compared to other injuries in the British Columbia health and social services sector. Methods The study used data on 42 080 time-loss workers’ compensation claims from the health care and social services sector in British Columbia during 2009–2014. Cox regression and quantile regression were used for time-to-event analysis and final RTW status was assessed at one year. Results The final cohort had 3173 violence-related claims (14.8%). Residential Social Services had the highest proportion of violence-related claims (34.2%). The effect of violence on RTW was greatest for counsellors and social workers, where 15.1% of workers with violence-related claims did not RTW compared to 8.0% with non-violent claims. For nurses, the largest occupation, 8.7% of workers with violence-related claims and 8.2% with non-violent claims did not RTW. Among injury types, violence is the strongest predictor for non-RTW for those with a mental illness. Among workers with a mental illness claim, 24.6% of those associated with violence did not RTW, whereas for those not associated with violence 15.0% did not RTW. Conclusion Findings suggest that violence is associated with poorer RTW outcomes in certain care settings and injury types. Future work will use matched analysis and number of disability days paid to investigate this association in more detail.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".