Declared Rationale for Cannabis Use Before and After Legalization for Nonmedical Use: A Longitudinal Study of Community Adults in Ontario
Bibliographic record
Abstract
Objectives: To examine the proportion of individuals using cannabis for medical purposes who reported nonmedical use of cannabis after it became legal to do so. Materials and Methods: We acquired data from the Population Assessment for Tomorrow's Health, the Cannabis Legalization Surveillance Study on a subpopulation of participants residing in Hamilton, Ontario, Canada, who reported using cannabis for medical purposes. Specifically, we acquired data 6 months before, and again 6 months after, legalization of cannabis for nonmedical purposes. We constructed a logistic regression model to explore the association between potential explanatory factors and endorsing exclusively nonmedical use after legalization and reported associations as odds ratios and 95% confidence intervals. Results: Our sample included 254 respondents (mean age 33±13; 61% female), of which 208 (82%) reported both medical and nonmedical use of cannabis (dual motives) before legalization for nonmedical purposes, and 46 (18%) reported cannabis use exclusively for medical purposes. Twenty-five percent ( n =63) indicated they had medical authorization to use medical cannabis, of which 37 (59%) also endorsed nonmedical use. After legalization of nonmedical cannabis, ∼1 in 4 previously exclusive cannabis users for medical purposes declared dual use (medical and nonmedical), and ∼1 in 4 previously dual users declared exclusively nonmedical use of cannabis. No individual with medical authorization reported a change to exclusively nonmedical use after legalization. Our adjusted regression analysis found that younger age, male sex, and lacking authorization for cannabis use were associated with declaring exclusively nonmedical use of cannabis after legalization. Anxiety, depression, impaired sleep, pain, and headaches were among the most common complaints for which respondents used cannabis therapeutically. Most respondents reported using cannabis as a substitute for prescription medication at least some of the time, and approximately half reported using cannabis as a substitute for alcohol at least some of the time. Conclusions: In a community sample of Canadian adults reporting use of cannabis for medical purposes, legalization of nonmedical cannabis was associated with a substantial proportion changing to either dual use (using cannabis for both medical and nonmedical purposes) or exclusively nonmedical use. Younger men without medical authorization for cannabis use were more likely to declare exclusively nonmedical use after 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".