Enhancing equity-oriented care in psychedelic medicine: Utilizing the EQUIP framework
Bibliographic record
Abstract
Psychedelic-assisted therapies are experiencing a re-emergence in mainstream medicine and mental health contexts. A wide variety of psychedelic-assisted therapy modalities are being utilized to address mental health issues such as substance use disorders, end of life anxiety, treatment-resistant depression, suicidality, PTSD and other conditions. The novel and inchoate acceptance of psychedelic-assisted therapies into mainstream medical and therapeutic realms raises questions of equity. Concerns have been raised that individuals and communities facing structural inequities are perhaps least able to access these treatments including Black, Indigenous and people of colour (BIPOC) as well as people who use drugs. Psychedelic-assisted therapies may exemplify the inverse law of care whereby services are most inaccessible to communities with the most need, especially if these therapies are regulated into the private sector. As legalization and wider implementation of these therapies appears to be imminent, now is a critical time to consider how health equity may be promoted within psychedelic medicine. This paper examines how the EQUIP Health Care approach (https://equiphealthcare.ca) may inform the development and provision of equity-oriented psychedelic-assisted therapies. The EQUIP approach seeks to reduce the effects of structural inequities on people's health, the impacts of discrimination and stigma, and the mismatches between usual approaches to care and the needs of people most affected by health and social inequities. Key dimensions of the EQUIP intervention include cultural safety, harm reduction, trauma and violence-informed care, and contextual tailoring.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.006 | 0.011 |
| 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".