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Record W2146708019 · doi:10.1111/josh.12137

The Co‐Use of Tobacco and Cannabis Among Adolescents Over a 30‐Year Period

2014· article· en· W2146708019 on OpenAlexafffundabout
Lauren Webster, Michael Chaiton, Maritt Kirst

Bibliographic record

VenueJournal of School Health · 2014
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of TorontoOntario Tobacco Research Unit
FundersOntario Ministry of Health and Long-Term Care
KeywordsCannabisTobacco useContext (archaeology)MedicineSubstance useEnvironmental healthAddictionLogistic regressionMental healthDemographyPsychiatryPopulationGeography

Abstract

fetched live from OpenAlex

BACKGROUND: This study explores the patterns of use and co-use of tobacco and cannabis among Ontario adolescents over 3 decades and if characteristics of co-users and single substance users have changed. METHODS: Co-use trends for 1981-2011 were analyzed using the Centre for Addiction and Mental Health Ontario Student Drug Use and Health Survey, which includes 38,331 students in grades 7, 9, and 11. A co-user was defined as someone reporting daily tobacco and/or cannabis use in the past month. Trends over time (by gender and academic performance) were analyzed with logistic regression. RESULTS: The prevalence of tobacco-only use, cannabis-only use, and co-use fluctuated considerably. During 1981-1993, there were more tobacco-only users than co-users and cannabis-only users; since 1993 the prevalence of tobacco use has decreased dramatically. Co-use prevalence peaked at 12% (95% confidence interval: 9, 15) in 1999, when prevalence of overall use of both substances was highest. In 2011, 92% of tobacco users also used cannabis, up from 16% in 1991. CONCLUSIONS: In 2011 nearly all students who smoke tobacco daily also use cannabis. Non-regular use of either substance is highest now compared with the past 3 decades. Contemporary tobacco and cannabis co-users are significantly different than past users. Youth prevention programs should understand the changing context of cannabis and tobacco among 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 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)0.002
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 score0.194
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.022
GPT teacher head0.334
Teacher spread0.312 · 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

Citations5
Published2014
Admission routes3
Has abstractyes

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