“Guarding their practice”: a descriptive study of Canadian nursing policies and education related to medical cannabis
Bibliographic record
Abstract
BACKGROUND: In Canada, federal regulations allow Nurse Practitioners (NPs) to authorize medical cannabis. Nursing regulatory bodies, however, have been hesitant to include medical cannabis within NPs' scope of practice. As the interest in cannabis increases, NPs have the potential to play a pivotal role in promoting the safe and appropriate use of cannabis. This study aimed to: summarize nursing policies in Canada related to medical cannabis; explore the perspective of nursing regulatory bodies regarding practice and policy issues related to medical cannabis; and examine the inclusion of medical cannabis content within Canadian NP curricula. METHODS: A descriptive study was conducted that comprised three phases. The first phase reviewed nursing regulatory bodies' existing policies related to medical cannabis. In the second phase, practice consultants from nursing regulatory bodies were interviewed regarding policies and practices issues related to medical cannabis. The interviews were analyzed using thematic analysis. The third phase was a national survey of NP program coordinators regarding inclusion of cannabis in curricula. Descriptive statistics summarized survey responses. RESULTS: Of the 12 nursing regulatory bodies in Canada, only 7 had policies or statements related to cannabis, with only Ontario allowing NPs to authorize medical cannabis. There was confusion among practice consultants regarding the role of nurses in the administration of medical cannabis and several barriers were identified regarding nursing engagement in care related to medical cannabis, including lack of knowledge and clinical guidelines. 60% of NP programs included cannabis in their curricula, however, less than half addressed the risks and benefits of medical cannabis and dosing and administration protocols. Limited faculty expertise was a barrier to including cannabis content in NP curricula. CONCLUSION: Nursing regulatory bodies must be proactive in developing policies and educational resources that will support nurses in providing safe and informed care related to cannabis. To ensure patients using medical cannabis receive consistent and safe care from nurses, harmonized regulations and policies are needed across all jurisdictions. Education programs must also provide updated knowledge and training for both registered nurses and NPs that will support them in providing non-judgemental and evidence-based care to the growing number of individuals using cannabis.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".