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Record W6990906325

Enacted stigma and HIV risk behaviors among sexual minority Indigenous youth in Canada, New Zealand, and the United States

2013· article· en· W6990906325 on OpenAlexaboutno aff

Bibliographic record

VenueResearchSpace (University of Auckland) · 2013
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousStigma (botany)CondomSexual minorityPsychological interventionHuman immunodeficiency virus (HIV)PopulationFocus group
DOInot available

Abstract

fetched live from OpenAlex

Enacted stigma has been linked to increased HIV risk behaviours among sexual minority youth, but despite higher rates of HIV and other STIs, there is very little research with Indigenous youth. In this study, secondary analyses of three population-based, school surveys were conducted to explore the associations between HIV risk and enacted stigma among sexual minority Indigenous youth in Canada, the US, and New Zealand. Data were analyzed and interpreted with guidance from Indigenous and sexual minority research team members, Indigenous advisory groups, and community consultations. In all three countries, Indigenous sexual minority youth were more likely to experience enacted stigma (such as bullying, discrimination, exclusion, harassment, or school-based violence) and report increased HIV risk behaviours (such as lack of condom use, multiple sexual partners, pregnancy involvement, and injection drug use) compared to heterosexual peers. Data were analyzed by age, gender, and sexual orientation, and for some groups, higher levels of enacted stigma was associated with higher HIV risk. The findings highlight the need for more research, including identifying protective factors, and developing interventions that focus on promoting resilience, addressing the levels of stigma and homophobic violence in school, and restoring historical traditions of positive status for Indigenous sexual minority people.

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)0.000
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 score0.062
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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