3-Year Follow-up of Lower Risk Cannabis Use Patterns: Evidence from a Longitudinal Survey
Bibliographic record
Abstract
Objectives: Following recommendations from the Lower Risk Cannabis Use Guidelines, we evaluated how lower risk cannabis use (late initiation and low use frequency) was associated with the risk of developing cannabis abuse/dependence over a 3-year follow-up period compared to 12-month abstinence (controls) or higher risk cannabis use (early initiation and higher use frequency). We also explored the effect of cannabis quantity. Methods: Data were obtained from the U.S. nationally representative survey, National Epidemiologic Survey on Alcohol and Related Conditions wave I (2001 to 2002) and wave II (2004 to 2005), which included 31,464 respondents with no lifetime history of cannabis abuse/dependence at the first interview. We applied multiple logistic regression and propensity score matching analyses to examine the association between different use patterns at wave I and cannabis abuse/dependence at wave II, adjusting for covariates. Lower risk cannabis use and the transition to higher use frequency were also assessed. Results: For propensity score analysis, lower risk cannabis use at wave I was associated with higher risk of cannabis use/dependence at wave II compared to controls (odds ratio [ OR]: 4.27; 95% confidence interval [95% CI], 1.57 to 11.61); however, there was no association with use frequency increase ( OR: 2.52; 95% CI, 0.88 to 7.17). Higher risk use had a greater risk of cannabis use/dependence than controls ( OR: 6.27; 95% CI, 2.56 to 15.38) and lower risk use ( OR: 2.69; 95% CI, 1.12 to 6.47). Logistic regression analyses showed similar results, except that lower risk use was significantly associated with use frequency increase ( OR: 2.49; 95% CI, 1.22 to 5.08). For the lower risk use group, 1 to 3 joints/day of use was significantly associated with cannabis abuse/dependence. Conclusions: We found that following recommended use patterns can significantly lower one’s risk of cannabis abuse/dependence. However, risk of cannabis abuse/dependence is still 4 times higher than staying abstinent. Updated recommendations on safe cannabis exposure levels are needed to guide cannabis use in the general population after cannabis legalization.
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 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".