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Record W1507908105 · doi:10.15173/nexus.v15i1.177

The Syphilis and HIV Connection: A Model in defense of an Anthropology of Sexually Transmitted Infections

2001· article· en· W1507908105 on OpenAlexvenueno aff
Jennifer L. Joss, Heather L. Pearcey, Tara V. Postnikoff

Bibliographic record

VenueNEXUS The Canadian Student Journal of Anthropology · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicHIV, TB, and STIs Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsSyphilisHuman immunodeficiency virus (HIV)Argument (complex analysis)Sexually transmitted diseaseSexual behaviorImmunologyReproductive healthMedicineVirologyPsychologyEnvironmental healthSocial psychologyPopulation

Abstract

fetched live from OpenAlex

This paper was initiated as an effort to improve our understanding of health-seeking behaviour in individuals with sexually transmitted infections. It quickly became apparent that the social, cultural, economic, biological and political issues that influence health-seeking behaviour in individuals with sexually transmitted infections greatly differed from those who have or had other diseases. Through a critique of the most current model describing sexually transmitted infection health-seeking behaviour developed by Aral and Wasserheit (1999), this paper presents a focused argument for an anthropology of sexually transmitted infections. The Aral and Wasserheit (1999) model fails to significantly describe the health-seeking behaviour of individuals infected with more than one sexually transmitted infection simultaneously. By examining aspects of pathocenosis and epidemiological synergy, it was found that the complex interactions between syphilis and HIV/AIDS changes the way we should study health-seeking behaviour for individuals with sexually transmitted infections in general. Therefore, while examining the syphilis/HIV paradigm, it became clear that an anthropology of sexually transmitted infections is in fact necessary.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.006
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 head0.032
GPT teacher head0.366
Teacher spread0.334 · 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
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueNEXUS The Canadian Student Journal of AnthropologySame topicHIV, TB, and STIs EpidemiologyFrench-language works237,207