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Record W2596778827 · doi:10.1177/0022042617699197

Substance Use Profiles Among Juvenile Offenders: A Lifestyles Theoretical Perspective

2017· article· en· W2596778827 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Drug Issues · 2017
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaSimon Fraser University
KeywordsEcstasyLatent class modelMultinomial logistic regressionMDMAHeroinPsychologySubstance useSubstance abusePsychiatryClinical psychologyCrack cocaineLogistic regressionMedicineDrug

Abstract

fetched live from OpenAlex

Base rates of illicit substances such as cocaine, crack cocaine, and heroin are typically low in community-based studies, which often inhibit more complex multivariate analysis. Additionally, single-item measures and aggregate scales mask within-group differences among those showing versatility in their substance use. Latent class analysis was used to model the substance use profiles of adjudicated female ( n = 98) and male ( n = 378) youth. Alcohol, marijuana, acid, mushrooms, ecstasy, cocaine, crack cocaine, heroin, crystal methamphetamine, and nonmedical use of prescription pills were used to define latent profiles of substance use. Three latent classes were identified that were qualitatively different across males and females. Multinomial logistic regression analyses indicated that time spent outside of the home of the biological parents, early substance use, and parental substance abuse were informative of the use of substances such as cocaine, crack cocaine, and heroin. Implications for more individualized treatment strategies are discussed.

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.

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 score0.024
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.325
Teacher spread0.291 · 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