Clinician views on and ethics priorities for authorizing medical cannabis in the care of children and youth in Canada: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The use of cannabis for medical purposes by pediatric patients is expanding across Canada; however, supporting evidence, federal regulations and treatment guidelines are lacking. To understand factors affecting treatment decisions in this landscape, we sought to delineate clinician perspectives, ethics priorities and values for cannabis authorization. METHODS: We sampled participants purposefully through Canadian Childhood Cannabinoid Clinical Trials listservs, which include the majority of pediatric oncologists and palliative care physicians practising in Canada, among many other pediatric physicians and clinicians. Inclusion criteria were being a practising clinician in Canada, involvement in the care of children and willingness to be interviewed regardless of stance on medical cannabis. In November and December 2020, we conducted semistructured interviews focusing on principles, values and priorities, including medical, professional, regulatory, evidentiary and social considerations, for authorizing medical cannabis to children. Interviews were recorded, transcribed and analyzed by means of deductive and inductive thematic methods. RESULTS: We conducted 18 interviews with a diverse group of clinicians representing a range of specialties within pediatric care, including neurology, palliative care, oncology, family medicine and pharmacology. The interviews yielded 4 themes and 12 subthemes related to a priori (medical, professional, regulatory, evidentiary and social themes) and emergent themes. The 4 themes of access, relationships and relational autonomy (autonomy within relationships), medically appropriate use and research priorities were grounded in principles of harm reduction. Participants described problematic authorization procedures that negatively affect patient use. Principles associated with relational autonomy were highlighted as a feature of open clinical communication. Benefits of appropriate medical uses weighed positively over risks, even in the context of potential effects on neurodevelopment. Participants expressed that more research is essential to align medical cannabis with biomedical standards. INTERPRETATION: Clinicians reported pursuing ethical use of medical cannabis for pediatric patients and prioritizing their safety under principles of harm reduction. There is a need for evidence about neurodevelopmental risks, support for research, treatment guidelines and greater knowledge about stakeholder perspectives to alleviate burdens related to use of medical cannabis for pediatric patients in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".