An Educational Workshop for Nurses to learn about Psychedelic Therapies
Bibliographic record
Abstract
The history of psychedelic research underscores the complex interplay between scientific discovery, cultural attitudes, and regulatory considerations (George et al., 2022). As investigations continue and taboos surrounding psychedelics gradually erode, the field is poised to redefine psychiatric treatments and deepen our understanding of consciousness and the human mind (George et al., 2022). This novel area of research has the potential to permeate the health and wellness industry. As healthcare providers, educating ourselves is the first step to actualizing this goal. As psychedelic research rapidly expands, nurses and other healthcare providers may be expected to have knowledge about these therapies and be involved in the provision of psychedelic-assisted psychotherapy (PAP). Despite the increasing establishment of psychedelic research and educational organizations in various academic institutions, nursing schools lack formal inclusion of education or training related to this rapidly developing field. In response to this gap, and in an effort to provide introductory education geared towards nurses in particular, I have developed a workshop focused on psychedelic medicines. This workshop serves to not only introduce nurses to the dynamic and emerging field of psychedelic-assisted psychotherapy (PAP), but also has the potential to ignite a deeper interest among nurses to actively engage in this sphere in many ways.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.104 | 0.046 |
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".