Bibliographic record
Abstract
HEALTH ISSUE: Exposure to violence as children or as adults places a woman at higher risk of poor health outcomes, both physical and psychological. Abused women use more health care services and have poorer social functioning than non-abused women. Knowledge of the prevalence of violence against women, and of which women are at risk, should assist in the planning of services for abuse prevention and treatment of the health consequences of abuse. KEY FINDINGS: The highest rates of any partner violence were in Alberta (25.5%) and British Columbia (23%). The lowest rates were in Ontario (18.8%). Women aged 15-24 had the highest rates in all regions in Canada, compared with older women. Aboriginal women in Manitoba/Saskatchewan and Alberta had higher rates of violence (57.2% and 56.6% respectively) than non-Aboriginal women (20.6%). Lower rates of partner-related violence were reported among women not born in Canada (18.4%) than among Canadian-born women (21.7%). Visible minority women reported lower rates of lifetime sexual assault (5.7%) than non-visible minority women (12.3%). Perceptions of violence may vary by ethnicity. DATA GAPS AND RECOMMENDATIONS: More information is required concerning the prevalence of violence among Aboriginal women, immigrant and refugee women, women with disabilities, lesbian women and pregnant women. Future national population-based surveys need better questions on the health consequences of violence and related resource utilization. Further research is needed to identify the health care system's role in prevention, management and rehabilitation as they relate to violence against women. Future programs and policies must be based on valid, reliable and comprehensive empirical data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.016 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".