The help‐seeking of women violent crime victims: findings from the Canadian violence against women survey
Bibliographic record
Abstract
We use Canadian data to examine the help‐seeking strategies of women dealing with the consequences of violent victimization. Consideration of the help‐seeking strategies of victimsmay provide insight into other decision‐making processes. The analytic framework integrates research on police reporting and intimate partner violence with the wider help‐seeking literature. This integration allows for an examination of the effect of the victim’s relationship to her offender on decisions to seek help from family, friends, doctors, social service agencies and the police. The research has two objectives. First, we aim to determine whether help‐seeking exists as isolated choices or whether there is a discernable set of help‐seeking strategies used by crime victims. Although many victims do not call the police, they often rely on family, friends, social service and mental health interventions.We find that those victims who report their victimizations to the police also seek support from family and friends. Second, we examine the correlates of these help‐seeking decisions. In doing so, we explore the effects of the offender relationship on decisions to seek help. We explore differences in help‐seeking across attacks by strangers, spousal offenders, dating offenders, and other known offenders. Our findings suggest that women victimized by a spousal offender are more likely than others to use a substantial help‐seeking strategy that includes disclosure to the police, doctors and social service agencies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".