Australian psychologists' attitudes towards psychedelic‐assisted therapy and training following a world‐first drug down‐scheduling
Bibliographic record
Abstract
INTRODUCTION: This study explores the attitudes of psychologists towards psychedelics and psychedelic-assisted therapy (PAT) following the world-first regulatory changes in 2023 in Australia which permitted psilocybin and 3,4-methylenedioxy-methamphetamine (MDMA) to be used in clinical services. METHODS: A purposive sample of 20 Australian psychologists was recruited using snowball sampling. Semi-structured interviews were conducted which explored participants' attitudes, knowledge and concerns about PAT. Data were coded and analysed to identify and develop theme categories. RESULTS: Most psychologists exhibited positive attitudes towards psychedelics and their therapeutic potential, viewing them as promising for addressing chronic mental health conditions like depression. However, there was a notable concern regarding the limited evidence on efficacy and potential adverse experiences, as well as the complexity of the individualised treatment protocol. Despite enthusiasm, many psychologists had limited detailed knowledge about the interventions themselves. The need for comprehensive education and training programs, including exposure to psychedelic experiences and credible higher education institutions, was emphasised to ensure competence in administering PAT. DISCUSSION AND CONCLUSIONS: Psychologists displayed notably positive attitudes towards PAT, likely reflecting both shifting perceptions of psychedelics and self-selection bias within the sample. Despite this optimism, concerns were raised about psychiatric risks and the necessity for comprehensive and reputable training and supervision. The cohort showed openness to both novel treatments and innovative training methods, underscoring the importance of enhancing educational frameworks to ensure effective implementation of PAT.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".