MétaCan
Menu
Back to cohort
Record W1988150214 · doi:10.1080/00952990601175102

Factors Associated with Public Injecting Among Users of Vancouver's Supervised Injection Facility

2007· article· en· W1988150214 on OpenAlexafffundabout
Ian McKnight, Benjamin Maas, Evan Wood, Mark Tyndall, Will Small, Calvin Lai, Julio Montaner, Thomas Kerr

Bibliographic record

VenueThe American Journal of Drug and Alcohol Abuse · 2007
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of British ColumbiaAIDS VancouverSt. Paul's Hospital
FundersHealth CanadaMcKnight Foundation
KeywordsSyringeMedicineLogistic regressionOdds ratioOddsPublic healthCohortInjection drug useDrugMultivariate analysisEnvironmental healthDemographyHuman immunodeficiency virus (HIV)UnivariateMultivariate statisticsDrug injectionFamily medicineInternal medicinePharmacologyPsychiatryStatisticsNursing

Abstract

fetched live from OpenAlex

BACKGROUND: We evaluated factors associated with public drug injection among a cohort of injection drug users (SEOSI) originally recruited from within Vancouver's supervised injecting facility (SIF). METHODS: We used univariate statistics and logistic regression to examine factors associated with public drug injection among SEOSI participants. FINDINGS: Between June 2004 and July 2005, 714 IDU were followed up as part of SEOSI. In multivariate analyses, factors associated with public drug injection included homelessness (adjusted odds ratio (aOR) = 3.10; p < .001), syringe lending (aOR = 5.39; p < .001), requiring help injecting (aOR = 1.60; p = .05), and reporting that wait times affected frequency of SIF use (aOR = 3.26; p < .001). INTERPRETATION: Persistent public injection was independently associated with elevated HIV risk behaviors, as well as programmatic factors that limit SIF use. SIF program expansion may further help to reduce persistent risk behaviors and the community concerns related to public injection drug use.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.053
GPT teacher head0.306
Teacher spread0.253 · 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

Citations60
Published2007
Admission routes3
Has abstractyes

Explore more

Same venueThe American Journal of Drug and Alcohol AbuseSame topicHIV, Drug Use, Sexual RiskFrench-language works237,207