Bibliographic record
Abstract
<i>Background</i>: In October 2018, Canada became the second country to legalize non-medical cannabis. However, medical cannabis has been legally available in Canada since 2001 and, in 2015, approximately 800,000 Canadians reported using cannabis for medical purposes. Mental health is a common reason reported for using medical cannabis. <i>Objectives</i>: The current study examined perceived mental health among four groups: (1) Non/ex-users; (2) Recent non-medical users; (3) Recent unauthorized medical users; and (4) Recent authorized medical users. <i>Methods</i>: A total of 867 Canadian cannabis users and nonusers aged 16 to 30 were recruited through an online consumer panel in 2017, one year before non-medical cannabis legalization. Logistic and multinomial regression models were fitted to examine differences among cannabis use status and mental health measures. All estimates represent weighted data. <i>Results</i>: Self-reported emotional and mental health problems were higher among unauthorized (83.9%) and authorized medical cannabis users (83.2%) compared to non-medical users and non/ex-users (44.5% and 39.5%, respectively). Medical users were more likely to report using cannabis to manage or improve mental health problems than non-medical users (<i>p</i> < .001). There were few differences between unauthorized and authorized medical users, and between non/ex-users and non-medical users. <i>Conclusions</i>: The findings highlight a discrepancy between the recommendation that individuals with some mental health problems should avoid cannabis and the widespread practice of using cannabis to manage mental health. Education and reduced stigma around using cannabis after legalization in Canada may help address users coming forwards regarding use of cannabis for mental health problems.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".