Competency by Design for Electroconvulsive Therapy in Psychiatry Postgraduate Training
Bibliographic record
Abstract
INTRODUCTION: Psychiatry is in the process of shifting curricula in postgraduate training to a competency-by-design approach. One core aspect of postgraduate psychiatry training is the knowledge and practice of electroconvulsive therapy (ECT). The aim of this study was to develop and validate the corresponding set of competencies that need to be developed during postgraduate training in psychiatry. METHODS: This study involves the proposal of a set of competencies by an ECT curriculum committee from the University Department of Psychiatry, based on the competency-by-design principles, followed by a modified Delphi process, to reach expert consensus on the proposed, modified, and added competencies. RESULTS: Six ECT experts meeting the preset criteria were recruited to the study from 6 academic centers across Canada and participated in the 2 Delphi rounds. Thirty-one competencies were proposed in the first round. Twenty-three proceeded to the second round by meeting 80% agreement on a score of ≥4 using a 5-point Likert scale. Three competencies required rewording based on qualitative feedback; accordingly, 10 new competencies were suggested. Thirty-five competencies were rated by experts and reached the threshold of agreement and rating. Cronbach α increased from 0.89 after the first round to 0.95 after the second iteration. DISCUSSION: Consensus was generated on 35 competencies that need to be achieved during postgraduate training in psychiatry. These competencies can serve as the basis for developing ECT curricula in postgraduate psychiatry training. The method used is feasible and can be adopted for the development of other competencies and curricula in psychiatry and other medical fields.
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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 | 0.044 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".