Psychedelics in PERIL: The Commercial Determinants of Health, Financial Entanglements and Population Health Ethics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it