Protocol-based assessment and management of first episode psychosis: Comparison of short and medium-term outcomes in psychopathology, quality of life, functioning and family burden across two sites in India
Bibliographic record
Abstract
BACKGROUND: Standard assessment and management protocols exist for first episode psychosis (FEP) in high income countries. Due to cultural and resource differences, these need to be modified for application in low-and middle-income countries. AIMS: To assess the applicability of standard assessment and management protocols across two cohorts of FEP patients in North and South India by examining trajectories of psychopathology, functioning, quality of life and family burden in both. METHOD: FEP patients at two sites (108 at AIIMS, North India, and 115 at SCARF, South India) were assessed using structured instruments at baseline, 3, 6 and 12 months. Standard management protocols consisted of treatment with antipsychotics and psychoeducation for patients and their families. Generalised estimating equation (GEE) modelling was carried out to test for changes in outcomes both across and between sites at follow-up. RESULTS: There was an overall significant improvement in both cohorts for psychopathology and other outcome measures. The trajectories of improvement differed between the two sites with steeper improvement in non-affective psychosis in the first three months at SCARF, and affective symptoms in the first three months at AIIMS. The reduction in family burden and improvement in quality of life were greater at AIIMS than at SCARF during the first three months. CONCLUSIONS: Despite variations in cultural contexts and norms, it is possible to implement FEP standard assessment and management protocols in North and South India. Preliminary findings indicate that FEP services lead to significant improvements in psychopathology, functioning, quality of life, and family burden within these contexts.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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 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".