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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".