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Record W2021633375 · doi:10.2190/de.41.2.a

Modeling Initiation into Drug Injection among Street Youth

2011· article· en· W2021633375 on OpenAlexaff
Élise Roy, Gaston Godin, Jean-François Boudreau, Philippe-Benoît Côté, Véronique Denis, Nancy Haley, Pascale Leclerc, Jean‐François Boivin

Bibliographic record

VenueJournal of Drug Education · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcGill UniversityInstitut National de Santé Publique du QuébecUniversité LavalUniversité de Sherbrooke
Fundersnot available
KeywordsPsychosocialSubstance abuseHeroinPsychologyClinical psychologyMedicinePsychiatryDrug

Abstract

fetched live from OpenAlex

This study aimed at examining the predictors of initiation into drug injection among street youth using social cognitive theory framework. A prospective cohort study based on semi-annual interviews was carried out. Psychosocial determinants referred to avoidance of initiation. Other potential predictors were: sociodemographic characteristics, relationships with injectors, parent's substance misuse, drug use patterns, homelessness, survival sex, sexual abuse. Independent predictors were identified using Cox proportional hazards regression models. Among the 352 participants, high control beliefs about avoidance of initiation was protective while younger age, daily alcohol consumption, heroin use, cocaine use, and survival sex all increased risk of initiation. Preventive strategies targeting street youth should both enhance youth's control beliefs and actual control over their substance use and improve their life conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.397
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations20
Published2011
Admission routes1
Has abstractyes

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