Drop out from out-patient mental healthcare in the World Health Organization's World Menta Health Survey initiative
Bibliographic record
Abstract
BACKGROUND: Previous community surveys of the drop out from mental health treatment have been carried out only in the USA and Canada. AIMS: To explore mental health treatment drop out in the World Health Organization World Mental Health Surveys. METHOD: Representative face-to-face household surveys were conducted among adults in 24 countries. People who reported mental health treatment in the 12 months before interview (n = 8482) were asked about drop out, defined as stopping treatment before the provider wanted. RESULTS: Overall, drop out was 31.7%: 26.3% in high-income countries, 45.1% in upper-middle-income countries, and 37.6% in low/lower-middle-income countries. Drop out from psychiatrists was 21.3% overall and similar across country income groups (high 20.3%, upper-middle 23.6%, low/lower-middle 23.8%) but the pattern of drop out across other sectors differed by country income group. Drop out was more likely early in treatment, particularly after the second visit. CONCLUSIONS: Drop out needs to be reduced to ensure effective treatment.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".