An analysis of financial hardship faced by patients with First Episode Psychosis, and their families, in an Indian setting
Bibliographic record
Abstract
BACKGROUND: The economic burden of psychotic disorders is not well documented in LMICs like India, due to several bottlenecks present in Indian healthcare system like lack of adequate resources, low budget for mental health services and inequity in accessibility of treatment. Hence, a large proportion of health expenditure is paid out of pocket by the households. OBJECTIVE: To evaluate the direct and indirect costs incurred by patients with First Episode Psychosis and their families in a North Indian setting. METHOD: Direct and Indirect costs were estimated for 87 patients diagnosed at AIIMS, New Delhi with first-episode psychosis (nonaffective) in the first- and sixth month following diagnosis, and the six months before diagnosis, using a bespoke questionnaire. Indirect costs were valued using the Human Capital Approach. RESULTS: Mean total costs in month one were INR 7991 ($107.5). Indirect costs were 78.3% of this total. Productivity losses was a major component of the indirect cost. Transportation was a key component of direct costs. Costs fell substantially at six months (INR 2732, Indirect Costs 61%). Respondents incurred substantial costs pre-diagnosis, related to formal and informal care seeking and loss of income. CONCLUSION: Families suffered substantial productivity loss. Care models and financial protection that address this could substantially reduce the financial burden of mental illness. Measures to address disruption to work and education during FEP are likely to have significant long-term benefits. Families also suffered prolonged income loss pre-diagnosis, highlighting the benefits of early and effective diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".