Which Psychotherapy Model Should be Used in Psilocybin Treatment for Depression?
Bibliographic record
Abstract
OBJECTIVE: Unipolar and bipolar depression severely impact millions of individuals worldwide, with a significant subset of cases remaining unresponsive to conventional treatments. Psilocybin-assisted psychotherapy (PAP) has demonstrated therapeutic efficacy; however, the optimal psychotherapeutic approach remains undefined, ranging from unstructured models rooted in historical practices to modern frameworks that are structurally tailored for depression. This narrative review proposes a conceptualization of psychotherapeutic models employed in existing interventional trials of PAP for depression and provides a preliminary comparison of their main characteristics and evidence for efficacy. METHODS: The online databases PubMed, PsycINFO, and Google Scholar were searched for interventional trials evaluating PAP for individuals with unipolar or bipolar depression. RESULTS: A total of 38 publications were reviewed, contributing to the conceptualization of two main types of psychotherapy models: 1) 'Specific' approaches (most commonly Acceptance and Commitment Therapy and Perceptual-Control Therapy) and 2) 'Non-specific' models of psychological support. Both models emphasize the critical role of the therapeutic alliance, yet differ in mechanistic focus, with specific models being developed to enhance psychological flexibility and non-specific models emphasizing the concept of the 'inner-healer.' Importantly, critical gaps in the literature were identified, including methodological limitations of current evidence and the need for standardized reporting guidelines. CONCLUSION: Although each PAP model differs, both may have clinical relevance in depression treatment. Future work should explore the standardized reporting of psychological interventions in PAP and comparative study designs to better evaluate non-specific and specific models and inform treatment guidelines.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".