Can microcredit help improve the health of poor women? Some findings from a cross-sectional study in Kerala, India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: This study examines associations between female participation in a microcredit program in India, known as self help groups (SHGs), and women's health in the south Indian state of Kerala. Because SHGs do not have a formal health program, this provides a unique opportunity to assess whether SHG participation influences women's health via the social determinants of health. METHODS: This cross-sectional study used special survey data collected in 2003 from one Panchayat (territorial decentralized unit). Information was collected on women's characteristics, health determinants (exclusion to health care, exposure to health risks, decision-making agency), and health achievements (self assessed health, markers of mental health). The study sample included 928 non elderly poor women. RESULTS: The primary finding is that compared to non-participants living in a household without a SHG member, the odds of facing exclusion is significantly lower among early joiners, women who were members for more than 2 years (OR = 0.58, CI = 0.41-0.80), late joiners, members for 2 years and less (OR = 0.60, CI = 0.39-0.94), and non-participants who live in a household with a SHG member (OR = 0.53, CI = 0.32-0.90). We also found that after controlling for key women's characteristics, early joiners of a SHG are less likely to report emotional stress and poor life satisfaction compared to non-members (OR = 0.52, CI = 0.30-0.93; OR = 0.32, CI = 0.14-0.71). No associations were found between SHG participation and self assessed health or exposure to health risks. The relationship between SHG participation and decision-making agency is unclear. CONCLUSION: Microcredit is not a panacea, but could help to improve the health of poor women by addressing certain issues relevant to the context. In Kerala, SHG participation can help protect poor women against exclusion to health care and possibly aid in promoting their mental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it