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Record W2979931490 · doi:10.1093/heapol/czz100

The cost of safe sex: estimating the price premium for unprotected sex during the Avahan HIV prevention programme in India

2019· article· en· W2979931490 on OpenAlexaff
Matthew Quaife, Aurélia Lépine, Kathleen Deering, Fern Terris‐Prestholt, Tara Beattie, Shajy Isac, Ramesh Paranjape, Peter Vickerman

Bibliographic record

VenueHealth Policy and Planning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of ManitobaSt. Paul's HospitalUniversity of British Columbia
FundersEconomic and Social Research CouncilBill and Melinda Gates Foundation
KeywordsCondomDemographyInstrumental variableHuman immunodeficiency virus (HIV)EarningsDeveloping countryPopulationEnvironmental healthMedicineDemographic economicsEconomicsEconomic growthSociologyFamily medicine

Abstract

fetched live from OpenAlex

There is some evidence that female sex workers (FSWs) receive greater earnings for providing unprotected sex. In 2003, the landscape of the fight against HIV/AIDS dramatically changed in India with the introduction of Avahan, the largest HIV prevention programme implemented globally. Using a unique, cross-sectional bio-behavioural dataset from 3591 FSWs located in the four Indian states where Avahan was implemented, we estimate the economic loss faced by FSWs who always use condoms. We estimate the causal effect of condom use on the price charged during the last paid sexual intercourse using the random targeting of Avahan as an instrumental variable. Results indicate that FSWs who always use condoms face an income loss of 65% (INR125, US$2.60) per sex act compared to peers providing unprotected sex, consistent with our expectations. The main finding confirms that clients have a preference for unprotected sex and that policies aiming at changing clients' preferences and at improving the bargaining power of FSWs are required to limit the spread of HIV.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.033
GPT teacher head0.386
Teacher spread0.352 · 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.

Study designQualitative
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

Citations16
Published2019
Admission routes1
Has abstractyes

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