Implementation of Maternal Health Financial Scheme in Rural Bangladesh
Bibliographic record
Abstract
It is indeed our pleasure to acknowledge the collaboration and financial contributions made by the Canadian International Development Agency (CIDA) through the United Nations Population Fund (UNFPA). We acknowledge the support, commitment and technical assistance of NIPORT, CIDA and UNFPA. We express our sincere thanks to the project partners: RTM International and ICDDR, B. We acknowledge the willingness of Central Hospital & Diagnostic Center and BRAC Suswastho Hospital, Habiganj to participate in the study. Thanks also to the owners of district and upazila level commercial drug stores who supplied medicine to the clients in exchange for their vouchers. We would like to thank district and upazila level government officials and the service providers who were partners in implementing the study. Service providers and staff of HFWCs, the Central Hospital & Diagnostic Center and BRAC Suswastho Hospital, Habiganj played a critical role in the implementation process. The authors are thankful to Dr. Ahmed Al-Sabir, Director (Research), NIPORT for his guidance and continued support at every step of the study. Besides being an active collaborator from NIPORT, his role as Coordinator of Demand Based Reproductive Health Commodity Project
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".