Factors Associated With Physical and Sexual Violence Among Canadian Women Living With Physical Disabilities
Bibliographic record
Abstract
We examined victimization data from a Canadian survey of 1,095 women with disabilities to determine the following: a) Who experienced abuse, b) the forms of abuse, and c) the factors associated with abuse. A convenience sample of Canadian women (18 years +) completed a mailed survey. Descriptive statistics were used to describe the sample and types of abuse (physical, emotional, or sexual). Sequential logistic regression was used to determine factors related to physical and/or sexual violence. Those who reported cultural identities other than Canadian (OR = 1.93, 95% CI = 1.12-3.32) were more likely to have reported experiencing physical and/or sexual violence, as were those with an annual household income less than $20,000 (OR = 3.21, 95% CI = 1.97-5.25) or between $20,000 and $49,999 (OR = 2.08, 95% CI = 1.29-3.36). Women with two or more health conditions (OR = 3.2, 95% CI = 1.93-5.32) and those who had some or most activities limited by pain were also more likely to report having experienced physical and/or sexual violence (OR = 1.61, 95% CI = 1.08-2.41). In contrast, women who had not received information about sexuality (OR = .68, 95% CI = .42-.96 and older women (OR = .46, 95% CI = .28-.73) were less likely to report having experienced physical and/or sexual violence. Our findings are important to public health professionals and practitioners in the detection and prevention of violence among women living with physical disabilities (WLD).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".