Cumulative Contexts of Vulnerability to Intimate Partner Violence Among Women With Disabilities, Elderly Women, and Immigrant Women: Prevalence, Risk Factors, Explanatory Theories, and Prevention
Bibliographic record
Abstract
Some groups of women are more vulnerable to intimate partner violence (IPV) due to particular risks and/or experiences: women with disabilities, elderly women, and immigrant women (DEI). Too often, their reality goes unnoticed, especially for those belonging to more than one of these groups. In this literature review, researchers used an intersectional approach to document the similarities and differences in how DEI women experience IPV, in terms of forms and consequences, as well as related risk factors, explanatory theories, and prevention strategies. Researchers selected 56 articles for review based on the following inclusion criteria: studies on adults living in a situation of IPV, studies on one of the three demographics under study (DEI), studies about one or multiple research questions, and studies based on empirical data relying on research methodology in either French or English. Researchers evaluated each selected article for its quality according to a chart that was specially developed for this review. The results highlight existing "intersections" between these groups to help understand the influence of belonging to more than one vulnerability group on these women's experiences with IPV. The importance to better training social workers and developing policies and programs that target the social determinants of health to prevent IPV experienced by DEI is also discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".