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Record W2117627740 · doi:10.1177/1090198108319891

Social Support Moderates the Relationship Between Gay Community Integration and Sexual Risk Behavior Among Gay Male Couples

2008· article· en· W2117627740 on OpenAlexaff
Stevenson Fergus, Megan A. Lewis, Lynae A. Darbes, Alex H. Kral

Bibliographic record

VenueHealth Education & Behavior · 2008
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologySocial supportSocial psychologySocial environmentDevelopmental psychologySexual identitySexual coercionHuman sexualityPoison controlSuicide preventionSociologyMedicineGender studies

Abstract

fetched live from OpenAlex

Few studies of partnered gay men consider the social context within which sexual behaviors occur or investigate positive aspects of the social environment that may offset factors that are related to risky sexual behaviors. Fewer still include assessment of both individuals making up couples. Using an ecological framework and an actor-partner multilevel analysis approach, the authors investigate how three dimensions of gay community integration are related to individual sexual risk behavior among 108 individuals in 54 couples. They then investigate how general social support and partner-provided, HIV-specific social support moderate these relationships. An individual's gay community social engagement and general social support interact to predict sexual risk behavior, such that the apparent protective effect of social support is more pronounced among those with less social engagement. The association between partner-reported general social support and safer sexual behaviors is more pronounced among those whose partners disclose their gay identity to more 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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.406
GPT teacher head0.518
Teacher spread0.111 · 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

Citations39
Published2008
Admission routes1
Has abstractyes

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