Psychiatric Residents’ Perspectives on Psychedelics and Psychedelic Assisted Therapy
Bibliographic record
Abstract
This study evaluated the attitudes, knowledge, and educational experiences of psychiatry residents regarding psychedelics and psychedelic-assisted therapy (PAT). In 2023, we distributed an anonymous survey to United States psychiatry residents, assessing demographics, knowledge, and opinions on psychedelics/PAT. The survey also included items asking respondents to retrospectively self-report how strongly psychedelic-related work and educational opportunities influenced their career choice and ranking of residency programs in the Match. Of the 109 respondents, most reported limited formal education on psychedelics during training but expressed a strong desire for more instruction. Most believed psychedelics hold promise for psychiatric disorders (83.49%), though fewer saw similar potential for substance use disorders (55.96%). Notably, 39.81% reported psychedelic-related educational or research opportunities influenced residency program rankings, and 39.25% indicated the possibility of treating patients with psychedelics influenced their decision to pursue psychiatry. Higher knowledge scores and stronger belief in psychedelics' therapeutic potential were associated with greater self-reported influence of psychedelic-related opportunities on program ranking. This study is limited by its small sample size and potential for self-selection and recall bias. Overall, psychiatry residents demonstrated optimism about psychedelics' therapeutic potential but reported inadequate training. Expanding psychedelic-focused educational content in residency could better prepare trainees for psychedelics' evolving role in psychiatry.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".