Can psychedelic-assisted psychotherapy play a role in enhancing motivation to change in addiction treatment settings?
Bibliographic record
Abstract
Abstract Despite growing availability of several evidence-based approaches in the treatment of substance use disorders, existing pharmacotherapy and psychosocial interventions continue to have significant limitations, such as low treatment retention rates and high rates of relapse. There is a need to develop new strategies and models to address these limitations and target underlying psychosocial drivers of addiction, such as motivation to change – a crucial factor in achieving positive addiction treatment outcomes. Re-emerging clinical evidence and literature signal the promise of psychedelic-assisted psychotherapies as being novel, adjunctive treatments for a range of mental health and substance use disorders, encouraging further research. However, there remains a lack of formally validated metrics to evaluate recovery capital and motivation, limiting interpretation of the growing psychedelic literature. This commentary describes the current state of this line of investigation and potential impact of psychedelic-assisted psychotherapy on enhancing motivation to change in addiction treatment, and the need for validated metrics to evaluate recovery motivation and capital to assess the potential for psychedelic-assisted psychotherapies to elicit positive, lasting changes in substance use behaviors among those seeking treatment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".