Electroconvulsive Therapy Across Nations
Bibliographic record
Abstract
OBJECTIVES: We aimed to characterize worldwide electroconvulsive therapy (ECT) practice and compare practice across nations and global regions. METHOD: Our anonymous survey was open on SurveyMonkey.com from January to June 2022. We sent invitations to providers identified using a Medicare provider database, an advanced PubMed search function, and professional group listservs. Participants were instructed to submit one survey per ECT site. Response frequencies were pooled by global region and compared using nonparametric methods. RESULTS: Responses came from 126 sites, mostly in the United States (59%, n = 74), Europe (18%, n = 23), Canada (10%, n = 12), and South/East Asia (6%, n = 8). With some exceptions, sites were broadly consistent in practice as indicated by: a likely shift internationally from bitemporal to right unilateral electrode placement; predominant use of pulse widths <1 ms; preference for seizure threshold titration over age-based dosing methods; widespread availability of continuation/maintenance ECT (97%); and frequent use of quantitative outcome measures for depressive symptoms (88%) and cognitive adverse effects (80%). CONCLUSIONS: This is the first, published survey that aimed to characterize worldwide ECT practice. With some exceptions, responses suggest a concordance in practice. However, responses were primarily from the Global North. To obtain a truly worldwide characterization of practice, future surveys should include more responses from the Global South.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".