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Record W2124105664 · doi:10.3109/10826084.2011.561516

A Comparison of Drug Use and Risk Behavior Profiles Among Younger and Older Street Youth

2011· article· en· W2124105664 on OpenAlexafffundabout
Scott E. Hadland, Brandon D. L. Marshall, Thomas Kerr, Ruth Zhang, Julio Montaner, Evan Wood

Bibliographic record

VenueSubstance Use & Misuse · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of British ColumbiaAIDS VancouverSt. Paul's Hospital
FundersNational Institute on Drug AbuseCanadian Institutes of Health Research
KeywordsBinge drinkingDepression (economics)DrugMedicinePsychiatryHarm reductionYoung adultSuicide preventionInjury preventionLogistic regressionPoison controlDemographyHuman factors and ergonomicsGerontologyEnvironmental healthPublic health

Abstract

fetched live from OpenAlex

Among 559 street youth recruited between 2005 and 2007 in Vancouver, Canada, young drug users (<21 years of age) were compared with older drug users (≥21 years) with regard to recent drug use and sexual practices using multiple logistic regression. Older youth were more likely to be male and of Aboriginal ancestry, to have more significant depressive symptoms, to have recently engaged in crack smoking, and to have had a recent history of injection drug use. Young drug users, by contrast, were more likely to have engaged in recent binge alcohol use. Efforts to reduce drug use-related harm among street youth may be improved by considering the highly prevalent use of "harder" drugs and risk for depression among older youth.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.340
Threshold uncertainty score0.676

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.135
GPT teacher head0.395
Teacher spread0.259 · 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 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

Citations47
Published2011
Admission routes3
Has abstractyes

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