Psychedelic-Assisted Psychotherapy After COVID-19: The Therapeutic Uses of Psilocybin and MDMA for Pandemic-Related Mental Health Problems
Bibliographic record
Abstract
The COVID-19 pandemic stands to have impacts on mental health and well-being that will extend beyond its formal resolution. Before COVID-19, mental health disorders were already challenging global healthcare systems, directly accounting for 7.4% of the total burden of disease (1, 2). An estimated 1 billion people worldwide suffer from a mental health disorder, with the two most common disorders—depression and anxiety—costing the global economy US$1 trillion per year (3). Stigma and limited treatment options have amounted to substantial unmet need and violations in human rights for people with mental health disorders (1, 4, 5). Looking ahead, heightened post-pandemic demand for mental healthcare signifies an urgent need to bolster clinical capacity by integrating novel, cost-effective approaches into existing systems (6). Emergent literature globally describes the diverse impacts of COVID-19 on mental health (7, 8). For instance, available data among hospitalized COVID-19 patients in China revealed that approximately 96% suffered post-traumatic stress symptoms (9). Studies among intensive care unit (ICU) patients with previous coronaviruses infer high rates of posttraumatic stress disorder (PTSD), depression and anxiety (30-40%) persisting months after discharge (10), with similar rates observed in patients infected with COVID-19 (11). Highly exposed individuals such as frontline healthcare workers are susceptible to similarly negative outcomes due to prolonged occupational stress, elevating risk of PTSD and suicidality (12–14). Importantly, post-pandemic mental disorders are not limited to individuals directly exposed to COVID-19. Rather, research documents PTSD symptoms among individuals who have been indirectly exposed by witnessing (e.g., via the media) or being confronted with the threat of death or serious illness (e.g., worry/anticipation about the future) (7). COVID-19 has significantly altered lives in ways that exacerbate drivers of mental health problems, with widespread uncertainty, increased experiences of grief and loss, social isolation, economic and housing instability, and decreased access to critical services related to lockdowns (6, 15). Further, available data on the impacts of COVID-19 on substance use patterns indicate increased use of alcohol and other substances in response to stress and negative emotions (8, 16, 17). Social connections are crucial for people struggling with addiction and comorbidities such as depression, and increased social disconnection represents a key risk factor for adverse outcomes (e.g., relapse and overdose) (1, 6, 18). The societal and economic consequences are tremendous, with structurally vulnerable groups at greatest risk of harms. For example, North America has seen dramatic spikes in fatal overdoses attributable to socio-structural conditions worsened by COVID-19 (18, 19), disproportionately impacting racialized groups (20). The legacy of mental health problems that will be left behind by COVID-19 incites innovative solutions to address rising rates of PTSD, depression, anxiety, addictions, and social disconnection. As such, we would be remiss not to consider a novel approach with anti-depressive, anxiolytic, and antiaddictive potential that may also foster a sense of social and environmental connectedness, known as psychedelic-assisted psychotherapy (21–24).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".