“Like the Wild West”: Health care provider perspectives on impacts of recreational cannabis legalization on patients and providers at a tertiary psychiatric hospital in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Legalization has increased cannabis availability in Canada. Research shows complex relationships between cannabis use and mental health, and a need for health care providers to engage with patients about cannabis use. Providers have noted gaps in knowledge and research on the medical effects of cannabis as barriers to service delivery. It is unclear how providers and patients in mental health care settings have been impacted by legalization. METHODS: From June 1 to July 2, 2021, we conducted a qualitative study involving semi-structured interviews with 20 health care providers in a range of roles (e.g., physicians, pharmacists, nurses) within a psychiatric hospital setting. Participants responded to open-ended questions with follow-up probes on various topics related to cannabis legalization. Topics included impacts on patient mental and physical health, clinical impacts, education and training, legal cannabis retail system and the medical cannabis access system. RESULTS: Thematic analysis identified several themes in the data. Participants reported that legalization has had some positive impacts relating to clinical care and cannabis safety. They also expressed concerns with increased rates of cannabis use, risks to mental health and ongoing challenges engaging with patients about cannabis. Participants made recommendations for medical educators and regulators (e.g., updated curriculums, clinical guidelines), the mental health care sector (e.g., implementation of standardized screening), government (e.g., public health campaigns, safe use guidelines), the medical cannabis access system (e.g., increased regulation, research), and the legal cannabis system (e.g., zoning changes, point-of-sale information). CONCLUSIONS: This study begins to address the paucity of data on impacts of legalization from mental health service delivery settings. Findings show that although legalization has had some positive impacts, there are ongoing patient concerns and unmet provider needs. More research is needed to understand the experiences of providers delivering care to populations experiencing mental health and/or substance use concerns who use cannabis in the post-legalization era.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".