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Record W2731458119 · doi:10.1177/1049732317716419

Gay Men’s Understanding and Education of New HIV Prevention Technologies in Vancouver, Canada

2017· article· en· W2731458119 on OpenAlexafffundabout
Ben Klassen, Nathan J. Lachowsky, Sally Y. Lin, Joshua Edward, Sarah Chown, Robert S. Hogg, David Moore, Eric Abella Roth

Bibliographic record

VenueQualitative Health Research · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of British ColumbiaPHS Community Services SocietySimon Fraser UniversityUniversity of VictoriaVancouver Native Health SocietyAIDS Vancouver
FundersNational Institute on Drug AbuseMichael Smith Health Research BCCanadian Foundation for AIDS Research
KeywordsThematic analysisStigma (botany)Variety (cybernetics)Public relationsHealth careHuman immunodeficiency virus (HIV)PopulationPsychologyEarly adopterThe InternetMedical educationMedicineQualitative researchPolitical scienceSociologyFamily medicineBusinessEnvironmental health

Abstract

fetched live from OpenAlex

Effective rollout of HIV treatment-based prevention such as pre-exposure prophylaxis and treatment as prevention has been hampered by poor education, limited acceptability, and stigma among gay men. We undertook a thematic analysis regarding the education sources and acceptability of these New Prevention Technologies (NPTs) using 15 semistructured interviews with gay men in Vancouver, Canada, who were early adopters of NPTs. NPT education was derived from a variety of sources, including the Internet, health care providers, community organizations, sexual partners, and peers; participants also emphasized their own capacities as learners and educators. Acceptable forms of NPT education featured high-quality factual information, personal testimony, and easy access. Stigma was highlighted as a major barrier. For public health, policy makers, and gay communities to optimize the personal and population benefits of NPTs, there is a need for increased community support and dialogue, antistigma efforts, early NPT adopter testimony, and personalized implementation strategies.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
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 integrity0.0000.000
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 head0.472
GPT teacher head0.608
Teacher spread0.136 · 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 designNot applicable
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

Citations15
Published2017
Admission routes3
Has abstractyes

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