MétaCan
Menu
Back to cohort
Record W3092215100

Comparison of Drug Policies & Recreational Marijuana Use in the United States, the United Kingdom & Canada: A Cross-Sectional Descriptive Study

2005· article· en· W3092215100 on OpenAlexaboutno aff
Monisha Jayakumar

Bibliographic record

VenueTopSCHOLAR (Western Kentucky University) · 2005
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyEnvironmental healthRecreational DrugRecreational useRecreational drug useDrugRecreationMedicinePolitical sciencePsychiatryLaw
DOInot available

Abstract

fetched live from OpenAlex

Drug laws and policies have certainly had an impact on countries, whether it is positive or negative seems to be a debatable and inflammable issue. This study endeavored to analyze the drug laws and policies of three comparable countries – the United States, the United Kingdom, and Canada – to assess if a difference exists in recreational marijuana use patterns among marijuana users from these countries. It was evident that the United States followed a prohibitional model and the United Kingdom and Canada favored varying degrees of decriminalization. In addition, demographic and lifestyle characteristics, legal history, and general well-being of the three samples were compared. An epidemiological cross-sectional descriptive study was undertaken to study adult recreational marijuana users from the three countries via the internet, from 1996 to 1997. The results of the study revealed no significant difference in marijuana use, demographics, and general well-being among the three samples, thereby, implying that the highly punitive laws of the United States sample had more legal problems consequent to drug use behavior. The stringent drug policies of the United States may explain the above finding. Several implications for drug policy reform in the United States emerged from this study.

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)0.001
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 score0.208
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.090
GPT teacher head0.342
Teacher spread0.252 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueTopSCHOLAR (Western Kentucky University)Same topicCannabis and Cannabinoid ResearchFrench-language works237,207