Prevalence of violence against women reported in a rural health region.
Bibliographic record
Abstract
INTRODUCTION: Violence against women in Canada is an important public health problem. Published research that reports prevalence of violence against women by province or region is limited, and estimates of the rates of violence experienced by rural women are sparse. METHODS: This study reports the results of a secondary analysis of data to examine the prevalence of physical and sexual assault reported by women in a rural health region in Alberta, Canada. The report of assault was then examined to determine its relationship to self-reported health conditions, behaviours and health service use. RESULTS: In this study, 5% of women reported experiencing physical assault in the last 12 months and 23% reported experiencing sexual assault in their lifetime. Younger women reported more assault than older women. Women who reported sexual assault were more likely to report having used illicit drugs. Women who reported physical assault within the last 12 months were significantly more likely to also report having accessed mental health services and emergency services within the past year. Most women had seen a general practitioner or family doctor within the last 12 months. CONCLUSION: We argue that an integrated community-based model of service that includes the health sector is necessary to address violence against women in rural areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".