Caregiver experiences of children living with a diagnosed neurological disability and using medical cannabis
Bibliographic record
Abstract
Medical cannabis (MC) has recently emerged as a potential treatment option for pediatric neurodevelopmental conditions and epilepsy. Medical cannabis within these conditions remains limited in evidence-based literature. Caregiver experience can play a valuable role in providing real-world evidence. Thus, this study sought to conceptualize primary caregivers' experiences using medical cannabis to treat neurological conditions in their children. A qualitative multiple-case study design was used to ascertain caregiver experiences. Twelve primary caregivers were interviewed to identify four themes: lack of support, perception of efficacy, positive impacts on children and caregivers, and contribution to real-world evidence from caregivers. Caregivers reported symptom improvement in their children and improved quality of life for their child and family. However, caregivers identified a lack of support from the healthcare system as a challenge. This study highlights that while medical cannabis shows promise as a potential treatment option, there is a great need for more research and subsequent healthcare provider education. Significant barriers to caregivers acquiring knowledge and healthcare provider support put patients at risk. The healthcare system must develop better educational programs regarding the potential role of MC (such as the benefits and side effects in different patient groups and regulatory framework for prescribing) to support children and their families better.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".