Emerging Psychedelic-Assisted Therapies: Implications for Nursing Practice
Bibliographic record
Abstract
Background Psychedelic-assisted therapy research is demonstrating unprecedented rates of success in treating mental illness, addictions, and end-of-life distress. This psychedelic renaissance is a turning point in how complex human conditions can be treated and has implications for nursing knowledge, advocacy, and practice internationally. Objective This article aims to explore the current state of knowledge in the field of psychedelic-assisted therapy and the practice implications for nurses. Methods A scoping review of the literature was undertaken with a focus on mental health, addictions, and palliative care indications. Commentaries, syntheses, and reviews from the last 20 years were included, as well as all relevant primary study results. We then explored what is known about the nurse's past and present role in this field. Results The nurse's role in psychedelic-assisted therapy and research has been hitherto mostly invisible and thus remains under-explored and undefined. The profession is ideally positioned, however, to contribute to the future of this promising field. Conclusion As advocates for safe, ethical, and interdisciplinary practice, nurses can lead the development of psychedelic-assisted therapy practice, ethics, research, advocacy, policy, and education. This article provides guidance and support for prescient nursing leadership in these areas.
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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 | 0.041 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".