Expanding Horizons for Psychedelic-Assisted Therapy & Management Scholarship: European Perspectives
Bibliographic record
Abstract
The global mental health crisis represents one of the grand challenges of our time. Rates of already-common conditions such as depression and anxiety rose by more than 25% in the first year of the COVID pandemic, adding to the nearly one billion people who were already living with a mental disorder. Depression is ranked by the World Health Organization (WHO) as the single largest contributor to global disability while anxiety ranks sixth. Lost productivity from depression and anxiety has been estimated to cost the global economy US $1 trillion per year and is forecast to reach $16 trillion by 2030. The global mental health crisis has engendered a mindful search for alternative treatments to the current pharmaceutical-centered approach, which has been unable to provide sustained relief for many who suffer despite the vast increase in the use of antidepressants and other psychiatric drugs in recent decades. Psychedelic-assisted therapy (PAT) has emerged as a leading option, and the psychedelic drug market is projected to generate $7.2 billion by 2029. For the past two years, we have run successful interdisciplinary symposia exploring the potential of PAT to refresh dominant organizational assumptions and foster new paradigms in the delivery of mental healthcare. Building on these efforts, we propose a new symposium for AoM 2025 to support the expansion of management scholarship on this critically important topic while leveraging AoM 2025’s European location. Europe offers a fertile landscape for organizational scholars to explore the complexities of PAT, informed by distinct regulatory, cultural, and socio-political contexts. Countries such as the UK, the Netherlands, and Germany have long played pivotal roles in advancing PAT through clinical trials, regulatory innovation, and public-private partnerships. The symposium will explore how European leaders in policy, advocacy, research, and media view the ecosystem of psychedelic-assisted therapy, and how these insights inform possibilities for organization scholars to engage generatively with this burgeoning field. The panelists – Henrik Jungaberle of the MIND Foundation (Germany), Tadeusz Hawrot of the Psychedelic Access and Research European Alliance, Josh Hardman of Psychedelic Alpha (UK), and Anne Philippi of the New Health Club (Germany) – will illuminate the interplay of scientific, organizational, and institutional influences in the emergence of an ethical and legal PAT industry from its underground roots.
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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 | 0.041 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 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".