Relationships Among Intimate Partner Violence, Work, and Health
Bibliographic record
Abstract
Intimate partner violence (IPV) is a major public health problem, and recent attention has focused on its impact on workers and workplaces. We provide findings from a pan-Canadian online survey on the relationships among IPV, work, and health. In total, 8,429 people completed the survey, 95.5% of them in English and 78.4% female. Reflecting the recruitment strategy, most (95.4%) were currently working, and unionized (81.4%). People with any lifetime IPV experience reported significantly poorer general health, mental health, and quality of life; those with both recent IPV and IPV experience over 12 months ago had the poorest health. Among those who had experienced IPV, about half reported that violence occurred at or near the workplace, and these people generally had poorer health outcomes. Employment status moderated the relationship between IPV exposure and health status, with those who were currently working and had experienced IPV having similar health status to those without IPV experience who were not employed. While there were gender differences in IPV experience, in the impacts of IPV at work, and in health status, gender did not moderate any associations. In this very large data set, we found robust relationships among different kinds of IPV exposure (current, recent, and lifetime), health and quality of life, and employment status, including the potentially protective effect of current employment on health for both women and men. Our findings may have implications for strategies to address IPV in workplaces, and should reinforce emerging evidence that IPV is also an occupational health issue.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".