Situational analysis of prevailing practices in the management of first‐episode psychosis in Chennai, India
Bibliographic record
Abstract
AIM: This paper aims to examine how existing mental health within the city of Chennai, India manages first-episode psychosis, to determine lacunae and barriers in providing effective early intervention and to make appropriate recommendations to improve the care of first-episode psychosis patients. METHODS: Interviews were held with 15 health professionals to capture information on current practices and facilities available for the management of first-episode psychosis. RESULTS: No specialized clinic or services were available for individuals with first-episode psychosis in Chennai, except one. Pharmacotherapy was the main treatment modality with psychological support to patients and families. Most common drugs used were Risperidone, Olanzapine, and Haloperidol in their recommended doses. General practitioners and paediatricians, due to inadequate training in mental health, referred patients with psychosis to mental health professionals. CONCLUSIONS: Equipping the existing mental health services to manage FEP and training all health professionals on psychosis will improve FEP management in Chennai.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".