A Descriptive Study of Data Collection Systems Used in Electroconvulsive Therapy Units in the Province of Quebec, Canada
Bibliographic record
Abstract
OBJECTIVES: This study aimed to describe the data collection systems routinely used by electroconvulsive therapy (ECT) units across the province of Quebec, Canada. METHODS: We conducted a descriptive, cross-sectional study. Using an online survey, 31 ECT units delivering inpatient or outpatient ECT treatments in the province of Quebec provided information on the data collection systems used, data recorded, data collection strategies, indicators of satisfaction, limitations of the current data collection systems, and expectations toward the improvement of ECT data collection. RESULTS: Most units routinely collected information on individuals receiving ECT treatments, mainly on the medical chart (80%) and in paper format (71%). Most units (88.9%) collected ECT data manually. Electroconvulsive therapy parameters are collected by 66% to 80% of units, but only 16% of them have computerized records. The main limitations of the current systems are as follows: (a) the low frequency of computerization, (b) the underutilization of data, and (c) difficulties in the integration of information from different ECT units. Although 83.3% were satisfied with the current data collection strategies, 80% had a very positive opinion about the development and implementation of an innovative ECT provincial data collection registry. CONCLUSIONS: An integrated ECT provincial data collection system could overcome the variability documented in existing strategies and respond to the current provincial needs and expectations. Also, an integrated ECT provincial data collection system could support both clinical research and quality assurance necessary to inform standards of ECT practice in Quebec.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".