Competencies for Repetitive Transcranial Magnetic Stimulation in Postgraduate Medical Education: Expert Consensus Using a Modified Delphi Process
Bibliographic record
Abstract
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is recommended in Canadian guidelines as a first-line treatment for major depressive disorder. With the shift towards competency-based medical education, it remains unclear how to determine when a resident is considered competent in applying knowledge of rTMS to patient care. Given inconsistencies between postgraduate training programmes with regards to training requirements, defining competencies will improve the standard of care in rTMS delivery. OBJECTIVE: The goal of this study was to develop competencies for rTMS that can be implemented into a competency-based training curriculum in postgraduate training programmes. METHODS: A working group drafted competencies for postgraduate psychiatry trainees. Fourteen rTMS experts from across Canada were invited to participate in the modified Delphi process. RESULTS: Ten experts participated in all three rounds of the modified Delphi process. A total of 20 items reached a consensus. There was improvement in the Cronbach's alpha over the rounds of modified Delphi process (Cronbach's alpha increased from 0.554 to 0.824) suggesting improvement in internal consistency. The intraclass correlation coefficient (ICC) increased from 0.543 to 0.805 suggesting improved interrater agreement. CONCLUSIONS: This modified Delphi process resulted in expert consensus on competencies to be acquired during postgraduate medical education programmes where a learner is training to become competent as a consultant and/or practitioner in rTMS treatment. This is a field that still requires development, and it is expected that as more evidence emerges the competencies will be further refined. These results will help the development of other curricula in interventional psychiatry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.201 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".