Electroconvulsive Therapy Teaching in Canada
Bibliographic record
Abstract
AIM: The objective of this study was to present survey data on the teaching of electroconvulsive therapy (ECT) in health care centers across Canada. METHODS: Of 1273 centers identified, 175 were found to practice ECT. These centers were asked to complete a questionnaire, and 107 (61%) of them answered 5 questions dealing specifically with ECT teaching. These questions were as follows: (1) Does your facility have an ECT teaching program for residents in psychiatry? (2) How is ECT taught to residents in psychiatry? (3) If direct supervision of the administration of ECT is a requirement of the psychiatry training program, is there a minimum number of supervised treatments or minimum duration of training period? (4) Do residents provide unsupervised ECT at your center? (5) Which other groups of learners, if any, are provided with orientation, teaching, or training in ECT? RESULTS: Sixty percent of respondents had no ECT teaching program for psychiatry residents. Pedagogical methods varied, ranging from direct observation of ECT treatments to directed readings. Few centers required a minimum number of supervised treatments. No resident-administered ECT is performed without direct supervision. Interestingly, various groups of health care professionals were often invited to participate in ECT training. CONCLUSIONS: The situation regarding ECT teaching continues to be a cause for concern given the noted absence of organized, structured, and mandatory programs. No resident administering ECT, however, goes unsupervised, which is in keeping with good practice. Electroconvulsive therapy is taught in many different ways, and teaching is accessible to different groups of health care professionals. However, much remains to be done to standardize ECT teaching to render this therapy available to all those who need it and to overcome the stigma and bias associated with it.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".