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Record W4379376600 · doi:10.1177/10778012231178001

Intimate Partner Violence Among International and Interprovincial Migrants: A Population-Based Analysis of Canadian Linked Immigration and Justice Data

2023· article· en· W4379376600 on OpenAlexafffundabout
Lixia Zhang, Wendy Au, Roheema Ewesesan, Alexa R. Yakubovich, Douglas A. Brownridge, Marcelo L. Urquía

Bibliographic record

VenueViolence Against Women · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsPublic Health OntarioUniversity of TorontoDalhousie UniversityUniversity of Manitoba
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsImmigrationResidenceDemographyIncidence (geometry)Economic JusticeDomestic violencePopulationMedicineHazard ratioPoison controlOccupational safety and healthGeographyInjury preventionDemographic economicsPolitical scienceEnvironmental healthSociologyLawEconomics

Abstract

fetched live from OpenAlex

Factors associated with IPV among immigrant women are not well understood. Using linked immigration and justice data, we compared the incidence of justice-identified IPV (JIIPV) among 58,564 international immigrant women born outside of Canada, 30,098 women born in other Canadian provinces (i.e., interprovincial migrants), and 88,662 long-term Manitoban resident women. International immigrant women had the lowest incidence of JIIPV compared to matched long-term Manitobans (adjusted hazard ratio (aHR) 0.49, 95% CI: 0.43-0.56) and interprovincial migrants (aHR 0.56, 95% CI: 0.43-0.73). Among immigrants, JIIPV varied substantially according to birthplace, increased with length of residence, and was less frequent among secondary immigrants.

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)0.001
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 score0.230
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.026
GPT teacher head0.315
Teacher spread0.289 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

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