Advancing education in interventional psychiatry: scoping review of simulation training and the future of virtual reality-based learning
Bibliographic record
Abstract
Objectives: Interventional psychiatric procedures such as electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS) have become increasingly important therapeutic options for managing severe or treatment-resistant mental illnesses. However, research suggests that gaps in training students in these techniques represent a rate-limiting step for their further dissemination and accessibility for the public. Studies have shown that the majority of psychiatry residents lack necessary competency and self-confidence in performing these treatments. Simulation based training has served as a gold standard for training procedural skills in medicine. Simulation-based training environments, particularly immersive reality technology (e.g., virtual reality [VR]), represent a promising novel avenue for trainees to develop the necessary skills for delivering these treatments. This scoping review discusses the current training in interventional psychiatry and how simulation-based training, specifically VR, can improve pedagogy in this area. Methods: In this scoping review, a literature search was conducted on the PubMed database using specific search terms such as "simulat*", "training", "ECT", "TMS", "neuromodulation", and "interventional psychiatry". The search was limited to studies with language in English from 1980 to 2023. Results: The initial search yielded 2094 articles, of which 4 evaluated the effectiveness of simulation approaches for ECT and were included in this review. No published studies were identified regarding VR-based education in ECT or rTMS. Conclusions: This scoping review provides an overview of the current landscape of pedagogical methods in interventional psychiatry and highlights the identified gaps in both the existing literature and the potential application of simulation-based environments, including VR, within this field. Considering the ongoing shift in medical education towards competency-based training, this review discusses the needs and benefits of VR-based simulators as an avenue to enhance competency in interventional psychiatry. Leveraging existing experience in the use of VR-based simulators in procedural skill acquisition in surgery and anesthesia, as well as recommendations on how to translate this approach to clinical training in psychiatry, are also discussed.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".