Can Microfinance-Based Poverty Alleviation Programs Help Patients with Severe Mental Illness?
Bibliographic record
Abstract
Background: While the social security programs offer financial assistance to patients with severe mental illness in high-income countries, no such systems exist in low- and middle-income countries. During recent years, poverty alleviation programs have been found to alleviate poverty in many countries. However, such programs have not been tried in persons with severe mental illness. We report 1-year outcomes of a microfinance program to alleviate poverty in patients with schizophrenia in a low-income country. Objectives: The objectives were to assess the feasibility and acceptability of a poverty alleviation program and to study the effect of the program on clinical and financial variables. Methods: Twenty-five (25) unemployed, young persons (19–35) with severe mental illness living with the family were recruited into a microfinance-based poverty alleviation program. Feasibility was assessed through recruitment and retention. Psychopathology and functioning were assessed through Positive and Negative Syndrome Scale (PANSS), Brief Psychiatric Rating Scale, and Global Assessment of Functioning at baseline and 12 months. Results: The program was feasible and acceptable, with excellent recruitment and retention rates. There were statistically significant improvements in PANSS-positive symptoms ( P < 0.000), PANSS-negative symptoms ( P < 0.000), PANSS-general score ( P < 0.000), and functioning ( P < 0.001). At 12 months, participants earned an average of $USD 40/month, with an average of $USD 10 spent on medication, $USD 12.5 on loan repayment, and $USD 17.5 contribution to family living. Conclusions: Poverty alleviation programs can be used to help younger persons with severe mental illness. However, this study has numerous limitations, and there is a need to conduct definitive trials in this area.
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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 | 0.000 | 0.000 |
| 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".