Factors Associated With Global Variability in Electroconvulsive Therapy Utilization
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to investigate the social and economic factors that contribute to global variability in electroconvulsive therapy (ECT) utilization and to contrast these to the factors associated with antidepressant medication rates. METHODS: Rates of ECT and antidepressant utilization across nations and data on health, social, and economic indices were obtained from multiple international organizations including the World Health Organization and the Organization for Economic Co-operation and Development, as well as from the published literature. To assess whether relationships exist between selected indices and each of the outcome measures, a correlational analysis was conducted using Pearson correlation coefficients. Those that were significant at a level of P < 0.05 in the correlation analysis were selected for entry into the multivariate analyses. Selected predictor variables were entered into a stepwise multiple regression models for ECT and antidepressant utilization rates separately. RESULTS: A stepwise multiple regression analysis indicated that government expenditure on mental health was the only significant contributor to the model, explaining 34.2% of global variation in ECT use worldwide. Human Development Index was the only variable found to be significantly correlated with global antidepressant utilization, accounting for 71% of the variation in global antidepressant utilization. CONCLUSIONS: These findings suggest that across the globe ECT but not antidepressant medication utilization is associated with the degree to which a nation financially invests in mental health care for its citizens.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".