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P1-S5.40 Sex cells: a pilot study investigating cell phone-based sexual networks among men who have sex with men in South India

2011· article· en· W2316483372 on OpenAlexaff
Laura H. Thompson, Souradet Y. Shaw, Kathleen Deering, Sushena Reza‐Paul, Robert Lorway

Bibliographic record

VenueSexually Transmitted Infections · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsAIDS VancouverUniversity of Manitoba
Fundersnot available
KeywordsMedicineMen who have sex with menPhoneSexual behaviorDemographyGynecologyFamily medicineClinical psychologySyphilisHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

Background A high HIV prevalence (19–32%) has been reported among men who have sex with men (MSM) in South India. Indeed, sexual networks play a central role in the spread of HIV in these communities but have rarely been studied because of intense social stigma and methodological and ethical challenges. Although cell phones are commonly used among MSM to contact sexual partners in India, few studies have explored the formation of such sexual networks. This study sought to understand the structure, context and evolution of cell phone-based sexual networks of MSM in three South Indian cities. Methods Sampling frames in the three cities were established using MSM contacts stored in the cell phones of community-based researchers (CBRs). Study participant “seeds” were randomly selected from these social networks. Seeds were asked to recruit their sexual partners, who completed surveys about their sexual practices with regular partners and 7-day partners. Network diagrams were constructed using non-nominal codes linking study participants. Results Cell phone contacts represent a useful resource for constructing social networks. Preliminary results indicate the diversity of sexual networks and sexual practices within these networks. Conclusions New community-based methods of exploring sexual networks were assessed, and sexual practices, partner concurrency, and risk behaviours were explored. This information can be used to tailor more specific services for MSM in these communities. As this methodology sampled from social networks, more hidden “individuals who do not access health services were included in the study.”

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.033
GPT teacher head0.256
Teacher spread0.223 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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