Delivery of Electroconvulsive Therapy in Canada
Bibliographic record
Abstract
OBJECTIVE: Electroconvulsive therapy (ECT) is an effective treatment for mood and other psychiatric disorders. Despite widespread use, the specifics of ECT practice in Canada are largely unknown. A nationwide survey designed to document current delivery was therefore conducted. METHOD: One hundred seventy-five Canadian ECT delivery sites were identified. A detailed questionnaire (13 pages, 76 questions grouped in 11 subheadings) was developed, translated into French, piloted, and then forwarded to all ECT centers. RESULTS: Return rate for the full questionnaire was 61%. Wide-ranging information pertaining to ECT was gathered. This article, which addresses the data specifically pertaining to ECT devices, electrical stimulus parameters and electrode placements, showed that many core aspects of ECT practice in Canada are in keeping with current recommendations. The use of old sine wave devices is virtually nonexistent. Electroencephalographic (EEG) monitoring of seizures is widespread. CONCLUSIONS: Specific concerns were identified, including the need for access to back-up devices at all centers, the lack of ECT credentialing requirements by Canadian hospitals, and a striking variation in dosing practices. An audit of Canadian practice and the development of a National Standards Document would be an essential next undertaking.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.003 | 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".