Medical cannabis in schools: A qualitative study on the experiences of clinicians
Bibliographic record
Abstract
Objectives: Guidance is lacking for medical cannabis use in Canadian schools in both legislation and approach; the impact of ambiguous policy on patient care is unknown. A qualitative study was undertaken to explore the experiences of clinicians who care for school-aged children who take medical cannabis. Methods: Semi-structured interviews were recorded and transcribed verbatim. Qualitative content analysis performed using the Dedoose qualitative software ascribed meaning units and codes, which were further consolidated into categories and subcategories. Results: Thirteen physicians were interviewed virtually, representing seven provinces in Canada. The physicians provided care for between five and hundreds of school-aged children who took medical cannabis. The most common indications were refractory seizure disorders and autism. The interviews provided rich descriptions on perceptions of medical cannabis in schools, and in general. Five overarching categories were identified across both domains including variability, challenges (subcategories: lack of knowledge, stigma, lack of policy, and pragmatic challenges), potential solutions (subcategories: treat it like other medications, communication, education, and family support), positive experiences and improvements over time. Conclusion: In Canada, cannabis-based medicine use in schools still faces important challenges. Effective education, communication, family support and policy refinements that allow cannabis to be treated like other prescription medications are recommended to improve the status quo. These findings will guide the C4T Medical Cannabis in Schools Working Group's future priorities and initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".