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Economic and cultural correlates of cannabis use among mid‐adolescents in 31 countries

2006· article· en· W2143650433 on OpenAlexaboutno aff
Tom ter Bogt, Holger Schmid, Saoirse Nic Gabhainn, Anastasios Fotiou, Wilma Vollebergh

Bibliographic record

VenueAddiction · 2006
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisPsychologyPsychiatryClinical psychologyEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

AIMS: To examine cannabis use among mid-adolescents in 31 countries and associations with per-capita personal consumer expenditure (PCE), unemployment, peer factors and national rates of cannabis use in 1999. DESIGN, PARTICIPANTS AND MEASUREMENT: Nationally representative, self-report, classroom survey with 22 223 male and 24 900 female 15-year-olds. Country characteristics were derived from publicly available economic databases and previously conducted cross-national surveys on substance use. FINDINGS: Cannabis use appears to be normative among mid-adolescents in North America and several countries in Europe. The life-time prevalence of cannabis use was 26% among males and 15% among females and was lowest for males and females in the former Yugoslav Republic (TFYR) of Macedonia: 2.5% and to 2.5%, respectively; and highest for males in Switzerland (49.1%) and in Greenland for females (47.0%). The highest prevalence of frequent cannabis use (more than 40 times in life-time) was seen in Canada for males (14.2%) and in the United States for females (5.5%). Overall, life-time prevalence and frequent use are associated with PCE, perceived availability of cannabis (peer culture) and the presence of communities of older cannabis users (drug climate). CONCLUSIONS: As PCE increases, cannabis use may be expected to increase and gender differences decrease. Cross-national comparable policy measures should be developed and evaluated to examine which harm reduction strategies are most effective.

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 score0.049
Threshold uncertainty score0.994

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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

Citations84
Published2006
Admission routes1
Has abstractyes

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