A National Needs Assessment of Canadian Nurse Practitioners Regarding Cannabis for Therapeutic Purposes
Bibliographic record
Abstract
Introduction: In Canada, the Access to Cannabis for Medical Purposes Regulations (ACMPR) has given nurse practitioners (NPs) the power to authorize cannabis for therapeutic purposes (CTP) to eligible patients. This expansion in NPs' scope of practice underscores the importance of delivering balanced, evidence-based education on cannabis to NPs. The aim of this national study was to assess NPs' knowledge and practice gaps related to CTP to inform the development of future education resources that increase NPs' clinical competence and improve patient care related to medical cannabis. Methods: This is a quantitative, descriptive exploratory design study. A national online survey of NPs was conducted from August 2013 to June 2014. NPs were recruited through email lists held by numerous Canadian nursing organizations. The survey was adapted from a previous national survey that assessed CTP educational needs among Canadian physicians. The survey assessed NPs' knowledge, experience, barriers, and attitudes related to CTP as well as preferred format for future CTP education. Results: The sample consisted of 182 NPs from across Canada. The largest knowledge gap was related to dosing and creating effective treatment plans for patients using CTP. The majority of respondents (76.3%) ranked the need for education on CTP to be either strong or very strong. Over half (57%) reported that they would be comfortable authorizing medical cannabis through the ACMPR; this number increased to 64% if they were to receive appropriate education. Conclusion: Nursing regulatory organizations, in partnership with academic institutions and government agencies, must work toward the development of educational and clinical competencies specific to CTP. Tailored education programs are needed to address the knowledge gaps held by NPs and the clinical barriers they face to including CTP as part of their care.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".