Psychedelic-Assisted Psychotherapy After COVID-19: The Therapeutic Uses of Psilocybin and MDMA for Pandemic-Related Mental Health Problems
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».