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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".