Effect of Closing Facilities on Electroconvulsive Therapy Use in Glasgow
Bibliographic record
Abstract
OBJECTIVES: : To assess the effect of closure of electroconvulsive therapy (ECT) centers on ECT use. Electroconvulsive therapy remains a recommended and effective treatment for mental disorders. Declining rates of ECT use in the United Kingdom have been observed over the last 20 years with anecdotal observations that use has declined as the result of centralization of provision. In Glasgow, there have been site closures in the north with no such rationing taking place in the south. METHODS: : A naturalistic retrospective survey of the number of ECT courses commenced each year in Glasgow, with a comparison made between the north and the south of the city. Data were available from 1996 to 2008. RESULTS: : Our analysis showed no change in the mean number of ECT courses commenced in southern Glasgow (period 1, 42.25; period 2, 41.83; period 3, 31; F = 1.369; P = 0.298). There was a significant reduction in the mean number of ECT treatments commenced in northern Glasgow (period 1, 91.25; period 2, 51; period 3, 33.33; F = 10.06; P = 0.04). CONCLUSIONS: : In northern Glasgow, where there have been 2 site closures since 1996, ECT use has declined. This trend was not replicated in the south of the city. This would suggest that the closure of ECT centers does reduce the use of ECT. However, there may be a number of confounding variables that could not be factored into the analysis because of lack of available data.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".