Medical cannabis in schools: The experiences of caregivers
Bibliographic record
Abstract
Objectives: Implementing medical cannabis (MC) into a child's daily routine can be challenging and there is a lack of guidance for its therapeutic use in schools in Canada. Our objective was to learn about the experiences of caregivers of school-aged children who require MC. Methods: Qualitative description was used and caregivers were interviewed about MC in schools and in general. The transcripts were entered into Dedoose software for qualitative analysis and content analysis was performed. Sentences and statements were ascribed line by line into meaning units and labelled with codes, and organized according to categories and subcategories. Results: Twelve caregivers of school-aged children who take MC participated. The most common reasons for treatment were drug-resistant epilepsy (DRE), autism, or other developmental disorders. Approximately half of the participants' children (n = 6) took MC during the school day and most (5/6) perceived their experiences to be positive or neutral but reported a lack of knowledge about MC. While data saturation was not reached regarding MC in schools, rich dialogues were garnered about MC in general and three categories were identified: challenges (subcategories stigma, finding an authorizer, cost, dosing, and supply); parents as advocates (subcategories required knowledge, attitudes, skills, and sources of information); and caregiver relief for positive outcomes. Conclusions: Caregivers demonstrate remarkable tenacity despite the many challenges associated with MC use. Education and practice change are needed to ensure that children using MC can benefit from or continue to experience its positive outcomes within the school environment and beyond.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".