Cannabis-Related Content in North American Post-Secondary Curricula: A Scoping Review
Bibliographic record
Abstract
Background: On August 11, 2016, the Canadian government announced the new Access to Cannabis for Medical Purposes Regulations (ACMPR) outlining reasonable cannabis use for medical purposes. Educators teaching cannabis-related content face challenges as the new legalization requires a shift away from a focus on substance abuse towards incorporating economic, social, and health promotion aspects. \nObjectives: To provide a report on what curricula incorporating the concept of cannabis are offered by post-secondary institutions as well as how and for what purpose is content is offered. \nMethod: A scoping review of published and grey literature was conducted to determine the extent of the available research and grey literature related to the concept of cannabis in postsecondary curricula in North America. Online search engines and multiple databases were used.\nResults: Peer-reviewed nursing articles emphasize teaching cannabis in terms of substance abuse treatment rather than knowledge about medical cannabis. Within English-speaking Canadian universities/colleges that have a nursing school, many cannabis-related courses are being developed. These include different disciplines such as pharmacology, agriculture, and marketing and are delivered in the format of courses, workshops/seminars, and public presentations in classroom or online. \nConclusion: This study suggested that a limited number of courses about medical cannabis are being offered in post-secondary institutions in North America. As legislation changes continue to impact post secondary education, curricula must be adapted to meet the new requirements.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".