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Record W3115690065 · doi:10.1093/geroni/igaa057.2561

Looking Ahead: Intersecting Influences on Older Gay Men Living With HIV

2020· article· en· W3115690065 on OpenAlexaffabout
Brian de Vries, Gloria Gutman, Tamara Sussman, Shari Brotman, Denis Dubé

Bibliographic record

VenueInnovation in Aging · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill UniversitySimon Fraser University
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)Focus groupSexual orientationPsychologyGerontologyMen who have sex with menInequalityQualitative researchHealth careIntersection (aeronautics)Gender studiesAged careDevelopmental psychologyMedicineSocial psychologySociologyFamily medicinePolitical science

Abstract

fetched live from OpenAlex

Abstract Older HIV-positive gay men live at the intersection of multiple inequalities—with cascading effects on their present and future lives. This qualitative study explored how they plan for their future, with a focus on Advance Care Planning—the process of reflecting/communicating preferences and values for future health and end-of-life care. Seven French-speaking gay men aged 55+ in Montreal, Canada participated in a focus group that was audio-recorded, transcribed and thematically analyzed in four steps. Findings suggest the intersection of sexual orientation and HIV-positive status exacerbated self-disclosure issues; the further addition of age led to preoccupation with day-to-day living and rendered these men vulnerable to social isolation. These tensions not only interfered with their capacities to talk about future care, but also created barriers to thinking about future care. These findings describe the multiple layers and compounding consequences of inequality among older gay men living with HIV.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.006
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.350
Teacher spread0.314 · 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 designQualitative
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
Published2020
Admission routes2
Has abstractyes

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