Comparison of Traditional Didactic Seminar to High-Fidelity Simulation for Teaching Electroconvulsive Therapy Technique to Psychiatry Trainees
Bibliographic record
Abstract
OBJECTIVES: Traditional training of electroconvulsive therapy (ECT) consists of a combination of didactic and hands-on demonstrations using ECT equipment. Our goal was to explore the potential of a high-fidelity patient simulator (HPS) to train these skills. To our knowledge, this is the first time an HPS has been used for skills training in psychiatry. METHODS: Nineteen psychiatry residents participated in this randomized controlled trial to compare traditional training (n = 9) versus training using an HPS (n = 10). Two blinded raters assessed performance using a newly developed checklist and global rating scale for this task (ECT-OSATS) (Objective Structured Assessment of Technical Skills). Residents also completed a pretest-posttest knowledge test and confidence survey. RESULTS: Residents in the HPS group performed significantly better in terms of ECT-OSATS when compared with the control group (P < 0.001). All 10 of the HPS group received a "pass" rating following training, whereas only 1 of the 9 control group received a "pass" rating. There were no significant group differences in posttest confidence (P = 0.21) or total knowledge gain scores from pretest to posttest (P = 0.36). CONCLUSIONS: The level of clinical skill acquired by trainees in psychiatry for performing ECT is significantly superior using HPS- based training, in contrast to the domains of knowledge and confidence, which appear to be equally imparted using either training modality. The acquisition of skills in administering ECT seems to be an independent variable in relation to a clinician's level of knowledge and confidence in performing ECT.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".