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Record W3003447423 · doi:10.1080/09687637.2020.1715920

Routes of cannabis administration among adolescents during criminal prohibition of cannabis in Canada

2020· article· en· W3003447423 on OpenAlexaffabout
Kat Kolar, Tara Elton‐Marshall, Robert E. Mann, Hayley A. Hamilton

Bibliographic record

VenueDrugs Education Prevention and Policy · 2020
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental HealthWestern UniversityUniversity of British Columbia
Fundersnot available
KeywordsCannabisAdministration (probate law)CriminologyPsychologyMarijuana smokingPsychiatryPolitical scienceSubstance abuseLawPolysubstance dependence

Abstract

fetched live from OpenAlex

This report assesses sociodemographic correlates of cannabis routes of administration (ROAs) among adolescents in 2017, one year prior to legalization of cannabis in Canada. We analyze a subsample of 809 students (Grades 9–12) from the Ontario Student Drug Use and Health Survey (OSDUHS) who used cannabis in the previous year. Pipes/bongs/waterpipes (81.8%) are the most prevalent ROA, followed by joints (73.8%) and edibles (42%). Approximately 70% of students report 2+ ROAs. Alcohol use in the previous year is associated with 2.28 (1.3–5.58 OR; 95% CI) times the odds of food/drink ROA and 2.91 (1.10–3.89; 95% CI) times the odds of joint ROA. Tobacco use is associated with 1.60 (1.07–2.41 OR; 95% CI) times the odds of blunt ROA, 2.07 (1.10–3.89; 95% CI) times the odds of pipe/bong/waterpipe ROA, and 1.75 (1.03–2.95 OR; 95% CI) times the odds of e-cigarette/vape pen/vaporizer ROA. Students who used alcohol have a rate 1.59 (1.08–2.36 IRR; 95% CI) times greater for total ROA count, and students who used tobacco have a rate 1.24 (1.09–1.40 IRR; 95% CI) times greater. Given young people’s vulnerability to adverse outcomes associated with cannabis use, it is important to track ROA trends to inform harm reduction and educational programing and to evaluate impacts of policy changes.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.320
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.308
Teacher spread0.296 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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