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Record W2941832381 · doi:10.1177/0091415019843456

The Global Impact of HIV on Sexual and Gender Minority Older Adults: Challenges, Progress, and Future Directions

2019· article· en· W2941832381 on OpenAlexaff
Charles A. Emlet, Kelly K. O’Brien, Karen I. Fredriksen‐Goldsen

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Toronto
FundersNational Institute on Aging
KeywordsGerontologyStigma (botany)Human immunodeficiency virus (HIV)Sexual minorityMedicineIncidence (geometry)PsychologyDemographySexual orientationPsychiatrySociologyFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

According to Joint United Nations Programme on HIV/AIDS (UNAIDS) data, 36.9 million people are living with HIV worldwide. Older adults, those aged 50 years and older, with HIV are increasing worldwide; however, the prevalence and incidence differ substantially across regions. The purpose of this article is to provide an overview of how HIV is impacting older adults globally, with a focus on sexual and gender minority older adults. The article is organized using the eight geographical regions from UNAIDS, with information on the prevalence and incidence among older adults. Among sexual and gender minority older adults, key risks are identified, including laws that criminalize same-sex relationships; issues of stigma and fear; and the concomitant lack of access and barriers to HIV testing, treatment, and prevention. Progress within each region toward the UNAIDS 90-90-90 targets is included, and suggestions for future directions of research and service delivery are made.

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)0.006
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.347
Teacher spread0.322 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2019
Admission routes1
Has abstractyes

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