Why psychedelic-assisted therapy studies in eating disorders risk missing the mark on outcomes: a phenomenological psychopathology perspective
Bibliographic record
Abstract
Psychedelic-assisted therapy (PAT) is an emerging intervention in psychiatry, for which there is preliminary evidence for effectiveness in eating disorders (EDs). The subjective psychedelic experience is considered an important driver of positive outcomes following PAT; however, conventional study design approaches often overlook many of the nuances inherent to the experience. Consequently, considerable information is lost between the first-person account and its scientific interpretation and documentation. Phenomenology-a philosophical and empirical approach to studying lived experience-offers tools to assess and understand the experiential mechanisms of PAT. This commentary advances the case for a phenomenological approach to PAT research in EDs, focusing on key domains of experience that underlie both ED psychopathology and psychedelic experiences, including embodiment, intersubjectivity, affectivity, temporality, spatiality and selfhood. We define and outline these phenomenological domains of EDs and psychedelic experiences and critically examine current measurement approaches. Following, we provide specific research recommendations, including phenomenologically grounded qualitative research and microphenomenology (i.e., the assessment of short-lived, pre-conscious experiences), to more fully capture psychedelic experiences and determine their significance for ED outcomes. The application of phenomenology to the PAT study design may contribute to a better understanding of how individuals experience PAT and generate testable hypotheses to advance psychedelic interventions.
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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 | 0.048 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.015 | 0.017 |
| 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".