Health System Strengthening Through Professional Midwives in Bangladesh: Best Practices, Challenges, and Successes
Bibliographic record
Abstract
In 2008, a cadre of professional midwives was introduced in Bangladesh. Since then, 120 midwifery educational programs have been established. There are 2,556 midwives serving at 667 government health facilities, and there are more midwives working in nongovernmental organizations and the private sector. This case study documents the process of establishing a midwifery profession with distinct midwifery expertise in Bangladesh and aims to guide other low- and middle-income countries in best practices and challenges. We describe the national administrative groundwork for the profession's launch, roll-out of an education program aligned with the International Confederation of Midwives, national deployment, enabling environments in deployment, and the professional association. Bangladesh's professional midwives' roles in humanitarian response and the COVID-19 pandemic are also discussed. The first and final authors were closely involved in supporting the government's establishment of the profession, and their direct experience is drawn upon to contextualize the topics. In addition, the authors conducted a desk review of documents that supported the profession's integration into the health system and documented its results. Both routine program data and existing research studies were reviewed. Outcomes show that midwives are deployed to 95% of government subdistrict hospitals. About 50% of these hospitals are fully staffed with 4 midwives, and within the hospitals, midwives are in charge of 90% of the maternity wards and attend 75%-85% of the births. Since the midwives' deployment, significant quality improvement for most World Health Organization indicators has been found, along with increases in service utilization. The experience of establishing a new midwifery profession in Bangladesh shows that it is possible for a lower middle-income country to introduce a globally standard midwifery profession, distinct from nursing, to improve quality sexual, reproductive, maternal, newborn, and adolescent health services in both humanitarian and development settings.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".