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A comparison of the determinants of safe injecting and condom use among injecting drug users

2003· article· en· W1981900666 on OpenAlexaff
Ted Myers, Margaret Millson, Janet Rigby, Marguerite Ennis, James M. Rankin, William R. Mindell, Steffanie A. Strathdee

Bibliographic record

VenueAddiction · 2003
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsAIDS VancouverUniversity of British ColumbiaUniversity of TorontoToronto Public Health
Fundersnot available
KeywordsCondomCasualPsychological interventionMedicineLogistic regressionNeedle sharingAgency (philosophy)Drug userFamily medicineEnvironmental healthDemographyHuman immunodeficiency virus (HIV)Psychiatry

Abstract

fetched live from OpenAlex

A sample of 582 injecting drug users were interviewed as part of an evaluation of an AIDS prevention programme for drug users. This paper examines the biographic and predispositional determinants of five HIV preventive behaviours--equipment sharing (not receiving and not giving) and and condom use (with regular partners, casual partners and sex clients). A two-stage sequential approach was adopted for a logistic regression analysis. Initially, to model each of the five preventive behaviours, biographical and drug use variables were entered. In a second set of models, behavioural predisposition factors were included. Age, drug use and prison experience correlate with variables in both models, although not consistently in the same direction. While a predisposition to reject sharing correlates with safer rejecting and condom use, the predisposition to safer sex only correlates with condom use. Needle exchange programmes that only target the individual would seem to be inadequate. To enhance targeted interventions changes in public and agency policy that create a social environment conductive to behaviour change are required.

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)0.000
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 score0.150
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.051
GPT teacher head0.352
Teacher spread0.300 · 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

Citations21
Published2003
Admission routes1
Has abstractyes

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