Disruption as an opportunity or threat: A qualitative analysis of factors influencing the attitudes of experts in serious illness care toward psychedelic-assisted therapies
Bibliographic record
Abstract
BACKGROUND: Psychedelic-assisted therapies (PAT) are emerging as a promising treatment for psycho-existential distress in patients with serious illness. A recent qualitative analysis of perspectives of 17 experts in serious illness care and/or PAT research identified divergent views on the therapeutic potential and safety of PAT in patients with serious illness. This paper further analyzes the factors that may influence these views. OBJECTIVES: To identify factors underlying the attitudes of experts in serious illness care and/or PAT toward PAT and its potential role in serious illness care. METHODS: Semi-structured interviews of 17 experts in serious illness care and/or PAT from the United States and Canada were analyzed to identify factors cited as influencing their views on PAT. RESULTS: Five factors were identified as influencing experts' attitudes toward PAT: perception of unmet need, knowledge of empirical studies of PAT, personal experience with psychedelics, professional background, and age/generation. In addition, an integrative theme emerged from the analysis, namely PAT's disruptive potential at 4 levels relevant to serious illness care: patient's experience of self, illness, and death; relationships with loved ones and health-care providers; existing clinical models of serious illness care; and societal attitudes toward death. Whether this disruptive potential was viewed as a therapeutic opportunity, or an undue risk, was central in influencing experts' level of support. Experts' perception of this disruptive potential was directly influenced by the 5 identified factors. SIGNIFICANCE OF RESULTS: Points of disruption potentially invoked by PAT in serious illness care highlight important practical and philosophical considerations when working to integrate PAT into serious illness care delivery in a safe and effective way.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".