A Feasibility Study on Demand Side Financing (DSF) in Maternal Healthcare Services at Rural Bangladesh
Bibliographic record
Abstract
It is well known that supply subsidies for health services often fail to benefit the most vulnerable women, children and the poor. Therefore, Demand Side Financing (DSF) mechanisms are intended to reduce the demand side barriers. In Bangladesh, about 92% of births are still delivered at home and skill birth attendants attend only 13.0 percent of births. About 69.0 percent of the poor households do not have access to any ANC compared to 22.0 percent of the richest quintile. It was hypothesized that various demand side factors contribute in reducing access of the poor to maternal healthcare. Objectives: The purpose of the study was to look at the feasibility of DSF scheme among the rural poor mothers by collecting information on demand side factors of maternal healthcare and also to assess the purchasing capacity and communities' behavior towards introducing DSF scheme. Methodology: The study covered all types of districts - plain, riverine, hilly and tribal. Primary data were collected from the pregnant mothers and mothers in the post natal period. Key informants interviews (KIIs) were conducted with the community and religious leaders, and public representatives. Population representatives sample (PRS) were collected from 6 areas including 450 pregnant and women in the neonatal period. Findings: The rate of utilization of ANC was 32.2 percent, PNC was 14.3 percent, and deliveries assisted by medically competent persons were 10.5 percent. The average cost of ANC was Tk.68.75 (US$1), PNC Tk.41.25 (US$0.60) and delivery Tk.650 (About US$10.0). Majority of the families met up the cost of delivery from savings (29.5 percent), followed by personal loan (4.5 percent) and selling of household goods or assets like cows, goats, trees or ornaments (50.0 percent). Conclusions: Findings suggest that introducing prepaid voucher scheme would increase utilization of maternal healthcare, empower people to make choices among different providers, increase quality of care or supply of goods make providers responsive to users and provide financial protection in the event of major illness. Considering the economical status of the households with the pregnant mothers some prepaid voucher scheme may be introduced. The proposed voucher in tentative and could be introduced in a sliding scale. Targeting the poor and choosing beneficiaries need to be a careful exercise for a developing country like Bangladesh.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".