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Record W7100250799

Violence against Canadian Women

2004· article· en· W7100250799 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsDomestic violenceOccupational safety and healthSexual violenceSuicide preventionPoison controlInjury preventionSexual abuse
DOInot available

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.413

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0110.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.002

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.

Opus teacher head0.016
GPT teacher head0.287
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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