Psychedelics in PERIL: The Commercial Determinants of Health, Financial Entanglements and Population Health Ethics
Bibliographic record
Abstract
The nascent for-profit psychedelic industry has begun to engage in corporate practices like funding scientific research and research programs. There is substantial evidence that such practices from other industries like tobacco, alcohol, pharmaceuticals and food create conflicts of interest and can negatively influence population health. However, in a context of funding pressures, low publicly funded success rates and precarious academic labor, there is limited ethics guidance for researchers working at the intersection of clinical practice and population health as to how they should approach potential financial sponsorship from for-profit entities, such as the psychedelic industry. This article reports on a reflective exercise among a group of clinician scientists working in psychedelic science, where we applied Adams' (2016) PERIL (Purpose, Extent, Relevant harm, Identifiers, Link) ethical decision-making framework to a fictionalized case of corporate psychedelic financial sponsorship. Our analysis suggests financial relationships with the corporate psychedelic sector may create varying degrees of risk to a research program's purpose, autonomy and integrity. We argue that the commercial determinants of health provide a useful framework for understanding the ethics of industry-healthcare entanglements and can provide an important population health ethics lens to examine nascent industries such as psychedelics, and work toward potential solutions.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.075 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| 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".