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Record W2144747779 · doi:10.1080/13691050701816714

Secondary HIV prevention among <i>Kothi</i> ‐identified MSM in Chennai, India

2008· article· en· W2144747779 on OpenAlexafffund
Venkatesan Chakrapani, Peter A. Newman, Murali Shunmugam

Bibliographic record

VenueCulture Health & Sexuality · 2008
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsCentre for Social InnovationUniversity of Toronto
FundersUniversity of TorontoUnited States Agency for International Development
KeywordsCriminalizationTamilMen who have sex with menPsychological interventionThematic analysisContext (archaeology)HarassmentSnowball samplingStigma (botany)MedicinePsychologyNonprobability samplingHuman immunodeficiency virus (HIV)Sexual transmissionQualitative researchFamily medicineSocial psychologyCriminologyPsychiatryEnvironmental healthSociologySyphilisPopulationGeography

Abstract

fetched live from OpenAlex

This study explored experiences and contexts of HIV risk and prevention among HIV-positive kothi-identified men in Chennai, India. In-depth, semi-structured interviews were conducted with 10 HIV-positive men and three service providers, recruited using purposive sampling. Interviews were audio-taped, transcribed in Tamil and translated into English. Data were analysed using a narrative thematic approach and constant comparative method. Misconceptions about HIV transmission; cultural taboos around discussing sexual behaviour and HIV; stigma related to same-sex behaviour; harassment; and the criminalization of consensual sex between men present formidable challenges to HIV prevention. Frank and open discussion about male-to-male sexual behaviour and living with HIV, which may support health and HIV prevention, may be dangerous in the context of pervasive risks due to stigmatization, violence and criminalization. Instead, culturally appropriate, multi-level interventions developed in collaboration with community stakeholders are needed to support HIV prevention among kothi-identified men in South India.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.392
Teacher spread0.338 · 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

Citations67
Published2008
Admission routes2
Has abstractyes

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