Pathways to care in first-episode psychosis in low-resource settings: Implications for policy and practice
Bibliographic record
Abstract
Developing countries such as India face a major mental health care gap. Delayed or inadequate care can have a profound impact on treatment outcomes. We compared pathways to care in first episode psychosis (FEP) between North and South India to inform solutions to bridge the treatment gap. Cross-sectional observation study of 'untreated' FEP patients (n = 177) visiting a psychiatry department in two sites in India (AIIMS, New Delhi and SCARF, Chennai). We compared duration of untreated psychosis (DUP), first service encounters, illness attributions and socio-demographic factors between patients from North and South India. Correlates of DUP were explored using logistic regression analysis (DUP ≥ 6 months) and generalised linear models (DUP in weeks). Patients in North India had experienced longer DUP than patients in South India (β = 17.68, p < 0.05). The most common first encounter in North India was with a faith healer (45.7%), however, this contact was not significantly associated with longer DUP. Visiting a faith healer was the second most common first contact in South India (23.6%) and was significantly associated with longer DUP (Odds Ratio: 6.84; 95% Confidence Interval: 1.77, 26.49). Being in paid employment was significantly associated with shorter DUP across both sites. Implementing early intervention strategies in a diverse country like India requires careful attention to local population demographics; one size may not fit all. A collaborative relationship between faith healers and mental health professionals could help with educational initiatives and to provide more accessible care.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".