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Record W1980767667 · doi:10.1080/13691058.2010.524247

Stigma and HIV risk among<i>Metis</i>in Nepal

2010· article· en· W1980767667 on OpenAlexaboutno aff
Erin C. Wilson, Sunil Babu Pant, Megan Comfort, Maria L. Ekstrand

Bibliographic record

VenueCulture Health & Sexuality · 2010
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institutes of HealthMinistry of Economy, Trade and IndustryNational Institute of Mental HealthStyrelsen för Internationellt Utvecklingssamarbete
KeywordsMetisOutreachContext (archaeology)Psychological interventionCondomLaw enforcementGeographyMedicineSocioeconomicsHuman immunodeficiency virus (HIV)Environmental healthPolitical scienceSociologyFamily medicineLawPsychiatry

Abstract

fetched live from OpenAlex

Similar to other parts of Asia, the HIV epidemic in Nepal is concentrated among a small number of groups, including transgender people, or Metis. This study was conducted to explore the social context of stigma among Metis in Nepal to better understand their risk for HIV. Fourteen in-depth interviews were conducted with Metis in Kathmandu, Nepal. We found that stigma from families leading to rural-urban migration exposed Metis to discrimination from law enforcement, employers and sexual partners, which influenced their risk for HIV. Specific HIV-related risks identified were rape by law enforcement officers, inconsistent condom use and high reported numbers of sexual partners. These data point to an immediate need to work with law enforcement to reduce violence targeting Metis. HIV prevention, housing and employment outreach to Metis in rural areas and those who migrate to urban areas is also needed. Finally, there is a need for more research to determine the prevalence of HIV among Metis, to explore risk within sexual networks and to better understand of the relationship between Metis and their families in order to develop future programmes and interventions.

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)0.001
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 score0.132
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.024
GPT teacher head0.388
Teacher spread0.365 · 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

Citations47
Published2010
Admission routes1
Has abstractyes

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