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Record W2098372648

Prevalence of violence against women reported in a rural health region.

2006· article· en· W2098372648 on OpenAlexaffabout
Wilfreda E. Thurston, Scott B. Patten, Laura Lagendyk

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineOccupational safety and healthDomestic violenceSuicide preventionPoison controlMental healthPublic healthInjury preventionSexual assaultSexual violencePsychiatrySexual abuseEnvironmental healthNursing
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.286
Teacher spread0.258 · 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 teacher head, 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

Citations19
Published2006
Admission routes2
Has abstractyes

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