The high risk of IPV against Canadian women with disabilities.
Bibliographic record
Abstract
BACKGROUND: Mounting evidence suggests that women with disabilities have a particularly high risk of experiencing violence by an intimate partner. This study examined the elevated risk for male-female intimate partner violence (IPV) against women with disabilities compared to women without disabilities across three large-scale Canadian surveys. An explanatory framework was tested that organized risk markers based on whether they referred to the context of the relationship between the couple (relationship factors), the victim (victim-related characteristics), or the perpetrator (perpetrator-related characteristics). MATERIAL/METHODS: The data employed in this study were from three surveys collected by Statistics Canada: the 1993 Violence Against Women Survey, and the 1999 and 2004 iterations of the General Social Survey. Descriptive analyses consisted of cross-tabulations with Chi-square tests of significance. Logistic regression was used to calculate zero-order odds ratios and to perform multivariate analyses. RESULTS: A pattern was found in which women with disabilities reported a significantly higher prevalence of violence than those without disabilities. The perpetrator-related characteristics were the only variables that reduced the elevated odds of violence against women with disabilities. Partners of women with disabilities were more likely to engage in patriarchal domination as well as possessive and jealous behaviors. CONCLUSIONS: The apparent importance of perpetrator-related characteristics (e.g., jealousy) suggests that future research should include a focus on what it is about the context of disability that makes these men more likely to engage in behaviors that are associated with IPV perpetration. Population-based efforts, professionals working with women who are victims, and professionals working with male perpetrators need to pay attention to the role of disability in IPV.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".