Predicting Patterns of Global Variations in Electroconvulsive Therapy Utilization
Bibliographic record
Abstract
Neuropsychiatric disorders, primarily depression, represent about 14% of the overall global burden of disease (4). Despite overwhelming evidence that electroconvulsive therapy (ECT) is not only safe but is effective in alleviating many burdensome psychiatric symptoms and treating illnesses, there are notable differences in the rate at which it is used globally (2,3). The current project investigated the extent to which ECT usage rates differ worldwide and possible reasons for this difference. Results indicate higher ECT utilization is independently associated with greater availability of psychiatrists and higher levels of social progress. However, step-wise multiple regression model indicated that government expenditure on mental health explains the majority of variation in ECT usage rates worldwide (R 2 =0.33, p=0.006). Societal stigma against mental illness may also play a role. Both socioeconomic and political factors influence ECT utilization. Efforts to improve the accessibility of ECT from a global perspective may need to address the three-variable model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".