Avatar Intervention for cannabis use disorder in a patient with schizoaffective disorder: A case report
Bibliographic record
Abstract
Considering the harmful effects of cannabis on individuals with a severe mental disorder and the limited effectiveness of current interventions, this case report showcases the beneficial results of a 10-session Avatar intervention for cannabis use disorder (CUD) on a polysubstance user with a comorbid schizoaffective disorder. Virtual reality allowed the creation of an Avatar representing a person significantly related to the patient’s drug use. Avatar intervention for CUD aims to combine exposure, relational, and cognitive behavioral therapies while practicing real-life situations and learning how to manage negative emotions and cravings. Throughout therapy and later on, Mr. C managed to maintain abstinence from all substances. Also, an improvement in the severity of CUD as well as a greater motivation to change consumption, were observed after therapy. As observed by his mother, his psychiatrist, and himself, the benefits of Avatar intervention for CUD extended to other spheres of his life. The drastic results observed in this patient could be promising as an alternative to the current treatment available for people with a dual diagnosis of cannabis use disorder and psychotic disorder, which generally lack effectiveness. A single-blind randomized control trial comparing the treatment with a classical intervention in a larger sample is currently underway to evaluate whether the results are reproducible on a larger sample.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".