Comparison of Drug Policies & Recreational Marijuana Use in the United States, the United Kingdom & Canada: A Cross-Sectional Descriptive Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".