The potential for medicinal cannabis to help manage challenging behaviour in people with intellectual disability: A perspective review
Bibliographic record
Abstract
BACKGROUND: Around 2% of the population have intellectual disabilities. Over one-third people with intellectual disabilities (PwID) present with 'challenging behaviour', which nosologically and diagnostically is an abstract concept. Challenging behaviour is influenced by a range of bio-psycho-social factors in a population, which is unable to suitably comprehend and/or communicate concerns. This predisposes to poor health and social outcomes. There is no evidence-based treatments for managing challenging behaviour. Cannabidiol (CBD) and tetrahydrocannabinol (THC) are being trialled for a range of disorders, which are over-represented in PwID and provoke challenging behaviours, such as severe epilepsy, spasticity, post-traumatic stress disorder, social phobia, pain, etc. METHODS: This perspective review explores the different conditions, which benefit from medicinal CBD/THC preparations, by analysing recent literature from neurobiological, pre-clinical and clinical studies related to the topic. The evidence is synthesised to build an argument of the therapeutic benefits and challenges of medicinal cannabis to manage severe challenging behaviour in PwID. RESULTS: There is developing evidence of medicinal CBD/THC improving psychiatric and behavioural presentations in general. In particular, there is emergent proof in certain key areas of influence of medicinal CBD/THC positively supporting challenging behaviour, for example in children with neurodevelopmental disorders. However, there are significant challenges in employing such treatments in vulnerable populations such as PwID. CONCLUSION: Further clinical research for the considered use of medicinal CBD/THC for challenging behaviour management in PwID is needed. Strong co-production with experts with lived experience is needed for further testing to be done in this exciting new area.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".