Evolution of Electroconvulsive Therapy Practice During the COVID-19 Pandemic
Bibliographic record
Abstract
ABSTRACT: The COVID-19 pandemic disrupted the provision of electroconvulsive therapy (ECT) across the globe, challenging ECT services to develop protocols that preserve access to this life-saving treatment. This systematic review maps the literature on global ECT practice during the COVID-19 pandemic. The English-language literature was searched through OVID (MEDLINE, PsycINFO, and Embase) on August 7, 2024. Included articles described ECT practice at ECT-providing centers anywhere in the world during the COVID-19 pandemic. Studies were critically appraised, and descriptive synthesis focused on ECT capacity, decision making, hospital resources, procedural modifications, and patient outcomes. Of the 297 identified articles, 61 met the inclusion criteria. In the published articles, 90.7% of articles reported a reduced treatment volume. ECT services were also shifted to prioritize inpatient treatments. Decision making was balanced between administrators and ECT unit staff, although only 2 articles (3.2%) reported the involvement of clinical ethicists in decision making. Other common challenges included staff shortages or redeployment (39.3%), personal protective equipment shortages (18.0%), and limited space availability (11.5%). Among studies that reported on it, relapse occurred in 85.0% articles due to ECT service disruptions. Significant variation in procedural modifications and mitigation strategies were observed, with limited consensus on best practices. To better understand these variations, we developed a decision matrix categorizing ECT service based on the transmission risks and resource availability. This review highlights the importance of evaluating long-term ECT disruptions and developing policies ensuring service continuity in future public health emergencies.
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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 | 0.020 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".