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Record W2789074328 · doi:10.1177/0306624x18758853

Correlates of Illicit Drug Use Among Indigenous Peoples in Canada: A Test of Social Support Theory

2018· article· en· W2789074328 on OpenAlexaffabout
Liqun Cao, Velmer S. Burton, Liu Liu

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsIndigenousIllicit drugSample (material)Test (biology)Environmental healthDrugMedicineGeographyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Relying on a national stratified random sample of Indigenous peoples aged 19 years old and above in Canada, this study investigates the correlates of illicit drug use among Indigenous peoples, paying special attention to the association between social support measures and illegal drug use. Results from multivariate logistical regression show that measures of social support, such as residential mobility, strength of ties within communities, and lack of timely counseling, are statistically significant correlates of illicit drug use. Those identifying as Christian are significantly less likely to use illegal drugs. This is the first nationwide analysis of the illicit drug usage of Indigenous peoples in Canada. The results are robust because we have controlled for a range of comorbidity variables as well as a series of sociodemographic variables. Policy implications from these findings 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.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.160
GPT teacher head0.353
Teacher spread0.193 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

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