Domestic Violence in the Canadian Workplace: Are Coworkers Aware?
Bibliographic record
Abstract
BACKGROUND: Domestic violence (DV) is associated with serious consequences for victims, children, and families, and even national economies. An emerging literature demonstrates that DV also has a negative impact on workers and workplaces. Less is known about the extent to which people are aware of coworkers' experiences of DV. METHODS: Using data from a pan-Canadian sample of 8,429 men and women, we examine: (1) awareness of coworker DV victimization and perpetration; (2) the warning signs of DV victimization and perpetration recognized by workers; (3) whether DV victims are more likely than nonvictims to recognize DV and its warning signs in the workplace; and (4) the impacts of DV that workers perceive on victims'/perpetrators' ability to work. RESULTS: Nearly 40% of participants believed they had recognized a DV victim and/or perpetrator in the workplace and many reported recognizing more than one warning sign. DV victims were significantly more likely to report recognizing victims and perpetrators in the workplace, and recognized more DV warning signs. Among participants who believed they knew a coworker who had experienced DV, 49.5% thought the DV had affected their coworker's ability to work. For those who knew a coworker perpetrating DV, 37.9% thought their coworker's ability to work was affected by the abusive behavior. CONCLUSION: Our findings have implications for a coordinated workplace response to DV. Further research is urgently needed to examine how best to address DV in the workplace and improve outcomes for victims, perpetrators, and their coworkers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".