Delivery of Electroconvulsive Therapy in Canada
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to document electroconvulsive therapy use in Canada with respect to treatment facilities and caseloads based on a survey of practice (Canadian Electroconvulsive Therapy Survey/Enquete Canadienne Sur Les Electrochocs-CANECTS/ECANEC) and to consider these findings in the context of guideline recommendations. METHOD: All 1273 registered hospitals in Canada were contacted, and 175 sites were identified as providing electroconvulsive therapy; these sites were invited to complete a comprehensive questionnaire. The survey period was calendar year 2006 or fiscal year 2006/2007. National usage rates were estimated from the responses. RESULTS: Sixty-one percent of the sites completed the questionnaire; a further 10% provided caseload data. Seventy were identified as general; 31, as university teaching; and 21, as provincial psychiatric/other single specialty (psychiatric) hospitals. Caseload volumes ranged from a mean of fewer than 2 to greater than 30 treatments per week. Estimated national usage during the 1-year survey period was 7340 to 8083 patients (2.32-2.56 per 10,000 population) and 66,791 to 67,424 treatments (2.11-2.13 per 1000 population). The diagnostic indications, admission status, and protocols for course end points are described. CONCLUSIONS: The usage rates are in keeping with earlier Canadian data and with those from other jurisdictions. The difficulty obtaining caseload data from individual hospitals is indicative of the need for standardized data collection to support both clinical research and quality assurance. The wide variation in protocols for number of treatments per course indicates a need for better informed clinical guidelines. The broad range of caseload volumes suggests the need to review the economies of scale in the field.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 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".