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Record W2025164421 · doi:10.1186/1742-4690-7-s1-p122

Stigma and HIV risk behaviors of transgender women in Nepal: implications for HIV prevention

2010· article· en· W2025164421 on OpenAlexaboutno aff
Erin C. Wilson, Sunil Babu Pant

Bibliographic record

VenueRetrovirology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
Fundersnot available
KeywordsMetisTransgenderHuman immunodeficiency virus (HIV)MedicineContext (archaeology)Psychological interventionPopulationStigma (botany)Environmental healthSex workGerontologyFamily medicineGender studiesPsychiatryGeographySociology

Abstract

fetched live from OpenAlex

The growing HIV epidemic among Metis (i.e. transgender women) in Nepal has important implications for this at-risk population and the country overall. In order to develop interventions targeting this population, researchers must better understand the unique cultural context within which risk behavior occurs. This study was conducted to explore the social context of HIV risk behavior among Metis in Kathmandu, Nepal. Qualitative data were collected using in-depth interviews with fourteen Metis. These data were taken from a larger study with a purposeful convenience sample of men who have sex with men in Kathmandu, Nepal. Seven Metis reported currently being sex workers, while seven reported not currently engaging in sex work but having a history of providing sex for money. Utilizing a phenomenological approach, we found that stigma towards Metis resulting in discrimination by family members, law enforcement, and employers had an important effect on HIV risk for Metis. Specific HIV-related risks identified were rape and abuse by law enforcement officers leading to inconsistent condom use due to fear of carrying condoms in public. Low access and ability to carry condoms paired with high reported numbers of sexual partners revealed an environment ripe for the spread of HIV among Metis and their partners.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.325
Teacher spread0.309 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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