Testing the Effectiveness of Implementing a Model of Mental Healthcare Involving Trained Lay Health Workers in Treating Major Mental Disorders Among Youth in a Conflict-Ridden, Low-Middle Income Environment: Part II Results
Bibliographic record
Abstract
Objectives: To report the outcomes of young people (aged 14-30 years) treated for major mental disorders in a lay health worker (LHW) intervention model in a rural district of conflict-ridden Kashmir, India. Methods: Over a 12-month follow-up, LHWs collected data on symptoms, functioning, quality of life and disability, and patients’ and families’ service engagement and satisfaction. Results: Forty trained LHWs (18 males and 22 females) identified 262 individuals who met the criteria for a diagnosis of a major mental disorder, connected them with specialists for treatment initiation (within 14 days), and provided follow-up and support to patients and families. Significantly more patients (14-30 years) were identified during the 14 months of the project than those in all age groups in the preceding 2 years. At 12 months, 205 patients (78%) remained engaged with the service and perceived it as very helpful. Repeated measures ANOVA showed significant improvements in scores on the global assessment of functioning (GAF) scale (F[df, 3.449] = 104.729, p < 0.001) and all 4 domains of the World Health Organization quality of life (WHOQOL) brief version (WHOQOL-BREF) of the survey—Physical F(df, 1.861) = 40.82; Psychological F(df, 1.845) = 55.490; Social F(df, 1.583) = 25.189; Environment F(df, 1.791) = 40.902, all ps < 0.001—and a decrease in disability (F[df, 1.806] = 4.364, p = 0.016). An interaction effect between time and sex was observed for the physical health domain of the WHOQOL-BREF. Discussion and Conclusions: Our results show that an LHW-based service model, implemented in a rural setting of a low-to-middle income region plagued by long-term conflict, benefits young people with major mental disorders. We discuss the implications of our findings in the context of similar environments and the challenges encountered.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".