Microcredit Membership and Self-Reported Healthcare Autonomy among Bangladeshi Women
Bibliographic record
Abstract
Participation in microcredit programs has so far received widespread research and policy attention in the context of health and empowerment among Bangladeshi women. However, not much is known regarding the relationship between participation in microcredit programs and healthcare autonomy (HA) among women. In the present study, we analyzed two nationally representative surveys (Bangladesh Demographic and Health Survey 2004 and 2014), to assess the relationships between MC membership and HA among adult women (n = 29163), while adjusting for various sociodemographic correlates. Self-reported healthcare decision-making autonomy was assessed by asking whether or not the participant had final say on her healthcare. The findings revealed that between 2004 (20.9%, 95%CI = 19.8, 22.0) and 2014 (14.1%, 95%CI = 13.3, 15.0), the proportion of women reporting HA decreased significantly, despite considerable improvements across several socioeconomic indices, including higher education enrollment and labor market participation. Between 2004 and 2014, the percentage of microcredit borrowers decreased for Grameen (18.9% vs. 10.7%) and BRAC (7.9% vs. 7.4%), while it increased for BRDB (0.9% vs. 7.0%). A multivariate regression analysis revealed that Grameen Bank membership was positively associated with reporting HA in both male- (OR = 1.16, 95%CI = 1.09, 1.23) and female-headed households (OR = 1.44, 95%CI = 1.13, 1.85). A positive association between microcredit membership and HA was also observed for BRAC (OR = 1.33, 95%CI = 1.20, 1.47) and BRDB (OR = 1.18, 95%CI = 1.09, 1.29), but in the male-headed households only. Further analysis indicated that membership with Grameen bank was the most important predictor of HA, followed by BRAC, BRDB, and ASA, with the degree of importance varying substantially between male- and female-headed households. In conclusion, these findings suggest the potential of microcredit programs to promote healthcare autonomy among Bangladeshi women and provide insights for further research, as to why certain programs are more effective than others.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".