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Record W2605608342 · doi:10.1080/13691058.2017.1309461

HIV-related syndemic pathways and risk subjectivities among gay and bisexual men: a qualitative investigation

2017· article· en· W2605608342 on OpenAlexafffund
Barry D. Adam, Trevor Hart, Jack Mohr, Todd Coleman, Julia R G Vernon

Bibliographic record

VenueCulture Health & Sexuality · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsCanadians Living with HIVWilfrid Laurier UniversityToronto Metropolitan UniversityUniversity of Windsor
FundersInstitute of Infection and Immunity
KeywordsSyndemicPsychologyAnxietyQualitative researchClinical psychologyDevelopmental psychologyHuman immunodeficiency virus (HIV)PsychiatryMedicineSociology

Abstract

fetched live from OpenAlex

Life history interviews were conducted with 40 gay and bisexual men to identify modes of syndemic experience and risk practice. Out of the interview narratives emerged one major and two minor modes of developmental pathway whereby syndemic conditions are navigated and expressed: (1) a combination of adverse childhood events with later episodes of depression and/or substantial substance use; (2) personal disruption that led to periods of depression and anxiety associated with the stresses of migration; and (3) a disorientation and an unravelling of life trajectory in the transition from family of origin to college or work. Risk practices fell into three high-risk modes: active and frequent engagement in condomless sex; unassertive deferment to a partner's initiation of condomless sex; and episodic risk combined with a risk reduction strategy. Three low risk modes were also identified: no recent condomlessness but multiple risk history in interview; a trajectory over time from high to low risk; and consistent low risk practice. These different modes of syndemic experience and risk management may have implications for identification of the effective HIV prevention tools that work best for different sets of men.

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)0.002
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 score0.311
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.098
GPT teacher head0.431
Teacher spread0.333 · 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

Citations27
Published2017
Admission routes2
Has abstractyes

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