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Record W4399546957 · doi:10.1089/lgbt.2023.0265

Prevalence, Determinants, and Trends in the Experience and Perpetration of Intimate Partner Violence Among a Cohort of Gay, Bisexual, and Other Men Who Have Sex with Men in Montréal, Toronto, and Vancouver, Canada (2017–2022)

2024· article· en· W4399546957 on OpenAlexafffundabout
Stephen Juwono, Jorge Luis Flores Anato, Allison L. Kirschbaum, Nicholas Metheny, Milada Dvořáková, Shayna Skakoon‐Sparling, David Moore, Daniel Grace, Trevor Hart, Gilles Lambert, Nathan J. Lachowsky, Jody Jollimore, Joseph Cox, Mathieu Maheu‐Giroux

Bibliographic record

VenueLGBT Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsInstitut National de Santé Publique du QuébecCommunity Based Research CentreUniversity of GuelphSante MontrealSt. Paul's HospitalCanadian AIDS Treatment Information ExchangePublic Health OntarioMcGill UniversityMcGill University Health CentreUniversity of TorontoToronto Metropolitan UniversityUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsCohortPsychologyLesbianDemographyMen who have sex with menGender studiesMedicineGerontologySociologyHuman immunodeficiency virus (HIV)Family medicine

Abstract

fetched live from OpenAlex

Purpose: Longitudinal data on the experience and perpetration of intimate partner violence (IPV) among gay, bisexual, and other men who have sex with men (GBM) are limited. We estimated the prevalence of past 6-month (P6M) physical and/or sexual IPV (hereafter IPV) experience and perpetration, identified their determinants, and assessed temporal trends, including the impact of the coronavirus disease (COVID)-19 pandemic.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.339
Teacher spread0.318 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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