Canadian nursing students and education in medical and recreational cannabis: a preliminary evidence
Bibliographic record
Abstract
OBJECTIVES: Explore the interest of Canadian undergraduate and graduate nursing students in medical (MC) and recreational cannabis (RC) education. METHODS: Transformative learning theory framed an online survey exploring sources of information; factors and learning modalities of increasing interest in learning about MC/RC; and future career plans regarding MC/RC in practice. Survey ran from September 2022 to February 2023. Descriptive statistics and content analysis were applied. RESULTS: Respondents (n=153) disclosed knowledge gaps in MC/RC regulations (90 %), effectiveness (88 %), and dosing best practices (86 %). Exposure to clinical opportunities and virtual resources were stimulating learning factors. CONCLUSIONS: A socially responsive curriculum is crucial to engage nursing students in becoming more knowledgeable about this topic and understand the nurse's role in enhancing practice quality. Implications for International Audience - The evidence provides a proactive approach to nursing educators in countries where cannabis is legal or in the process of being legalized.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".