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Record W4387744194 · doi:10.9745/ghsp-d-23-00081

Health System Strengthening Through Professional Midwives in Bangladesh: Best Practices, Challenges, and Successes

2023· article· en· W4387744194 on OpenAlexfundno aff
Farida Begum, Rowsan Ara, Amirul Islam, Stephanie Marriott, Anna Williams, Rondi Anderson

Bibliographic record

VenueGlobal Health Science and Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersGlobal Affairs CanadaMinistry of Health and Family WelfareUnited Nations Population Fund
KeywordsMedicineBest practiceNurse-MidwivesNursingMEDLINEBusinessFamily medicineEconomic growthEnvironmental healthPregnancyPolitical science

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.004
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.100
GPT teacher head0.452
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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