Interventional Psychiatry and Neurotechnologies: Education and Ethics Training
Bibliographic record
Abstract
The last two decades have seen dramatic growth in the application of procedurally based interventions for treating refractory psychiatric conditions, leading to interest in developing the foundations for the subspecialty of "Interventional Psychiatry." However, there is cause for concern that the rate of expansion of clinical advances in this field may be outpacing the ability of postgraduate curricula to provide sufficient exposure to and teaching and supervision of these treatments. The paucity of adequately trained practitioners in Interventional Psychiatry further exacerbates inequities in the ability of eligible patients to access and benefit from these approaches. This paper explores the rates of utilization of Interventional Psychiatry treatments, the current state of education in these treatments, and the role that training can play in translating scientific advances in this area to ensure equitable access and maximum impact at a population level. The majority of the discussion is centered on electroconvulsive therapy (ECT), the most established and available of these treatments, highlighting how enhancing education and training in ECT can reduce barriers to its utilization. It is argued that innovations in pedagogical approaches for disseminating the learning of these procedures are needed to increase the current low rates of competency in these treatments and can facilitate the more rapid dissemination of other Interventional Psychiatry approaches and neurotechnologies, such as repetitive transcranial magnetic stimulation, ketamine, deep brain stimulation, and focused ultrasound.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".