Examination of cannabis use patterns, heavy user characteristics, and cannabis-related harms: results from 2012-2013 US national cross-sectional survey data
Bibliographic record
Abstract
This thesis aimed to examine cannabis use patterns by quantity in the United States, identify key characteristics of the heaviest cannabis users, and conduct an initial assessment of whether the prevention paradox may hold for cannabis use in the United States. Using data from the National Epidemiologic Survey on Alcohol and Related Conditions – III, findings suggest that a small portion of the cannabis-using population consumes the majority of the yearly cannabis supply in the United States. Characteristics that affect the odds of being a heavy cannabis user include age, sex, personal income, education level, age of initiation, and the presence of a cannabis use or nicotine use disorder. A larger absolute number of cases experience cannabis-related harms in the low-to-moderate-using group compared to the heaviest-using group. However, a higher percentage of heavy cannabis users experience cannabis-related harms. Therefore, a dual-pronged approach of both targeted and population-based strategies may be appropriate.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".