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Record W2175205982 · doi:10.1016/j.jana.2015.11.005

HIV Risk Behaviors, Perceptions, and Testing and Preexposure Prophylaxis (PrEP) Awareness/Use in Grindr-Using Men Who Have Sex With Men in Atlanta, Georgia

2015· article· en· W2175205982 on OpenAlexfundno aff
William C. Goedel, Perry N. Halkitis, Richard E. Greene, DeMarc A. Hickson, Dustin T. Duncan

Bibliographic record

VenueJournal of the Association of Nurses in AIDS Care · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersCenters for Disease Control and PreventionNational Institute of General Medical SciencesNational Institute of Mental HealthSchool of Medicine, New York UniversityYork UniversityNew York University
KeywordsMen who have sex with menPre-exposure prophylaxisMedicineHuman immunodeficiency virus (HIV)Psychological interventionAtlantaIncidence (geometry)DemographyGerontologyTransmission (telecommunications)Environmental healthFamily medicine

Abstract

fetched live from OpenAlex

Geosocial-networking smartphone applications such as Grindr can help men who have sex with men (MSM) meet sexual partners. Given the high incidence of HIV in the Deep South, the purpose of our study was to assess HIV risk and preexposure prophylaxis (PrEP) awareness and use in a sample of HIV-uninfected, Grindr-using MSM residing in Atlanta, Georgia (n = 84). Most (n = 71; 84.6%) reported being somewhat or very concerned about becoming HIV infected. Most (n = 74; 88.1%) had been tested for HIV in their lifetimes. About three fourths (n = 65; 77.4%) were aware of PrEP, but only 11.9% currently used the medication. HIV continues to disproportionately impact MSM and represents a significant source of concern. As the number of new infections continues to rise, it is important to decrease risks associated with acquisition and transmission of HIV by increasing rates of HIV testing and the use of biobehavioral interventions such as PrEP.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.018
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.321
Teacher spread0.299 · 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

Citations69
Published2015
Admission routes1
Has abstractyes

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