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Record W2969935701 · doi:10.1136/bmjoq-2018-000596

Quality of care during childbirth at public health facilities in Bangladesh: a cross-sectional study using WHO/UNICEF ‘Every Mother Every Newborn (EMEN)’ standards

2019· article· en· W2969935701 on OpenAlexfundno aff
Sk Masum Billah, Mohiuddin Ahsanul Kabir Chowdhury, Abdullah Nurus Salam Khan, Farhana Karim, Aniqa Hassan, Nabila Zaka, Shams El Arifeen, Alexander Manu

Bibliographic record

VenueBMJ Open Quality · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersInternational Centre for Diarrhoeal Disease Research, BangladeshUNICEFStyrelsen för Internationellt UtvecklingssamarbeteGlobal Affairs CanadaBill and Melinda Gates FoundationMinistry of Health and Family WelfareDepartment for International Development
KeywordsMedicineChildbirthReferralHealth facilityCross-sectional studyHealth carePublic healthNursingFamily medicineEnvironmental healthMedical emergencyPopulationPregnancyHealth services

Abstract

fetched live from OpenAlex

BACKGROUND: This manuscript presents findings from a baseline assessment of health facilities in Bangladesh prior to the implementation of the 'Every Mother Every Newborn Quality Improvement' initiative. METHODOLOGY: A cross-sectional survey was conducted between June and August 2016 in 15 government health facilities. Structural readiness was assessed by observing the physical environment, the availability of essential drugs and equipment, and the functionality of the referral system. Structured interviews were conducted with care providers and facility managers on human resource availability and training in the maternal and newborn care. Observation of births, reviews of patient records and exit interviews with women who were discharged from the selected health facilities were used to assess the provision and experience of care. RESULTS: Only six (40%) facilities assessed had designated maternity wards and 11 had newborn care corners. There were stock-outs of emergency drugs including magnesium sulfate and oxytocin in nearly all facilities. Two-thirds of the positions for medical officers was vacant in district hospitals and half of the positions for nurses was vacant in subdistrict facilities. Only 60 (45%) healthcare providers interviewed received training on newborn complication management. No health facility used partograph for labour monitoring. Blood pressure was not measured in half (48%) and urine protein in 99% of pregnant women. Only 27% of babies were placed skin to skin with their mothers. Most mothers (97%) said that they were satisfied with the care received, however, only 46% intended on returning to the same facility for future deliveries. CONCLUSIONS: Systematic implementation of quality standards to mitigate these gaps in service readiness, provision and experience of care is the next step to accelerate the country's progress in reducing the maternal and neonatal deaths.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.144
GPT teacher head0.473
Teacher spread0.329 · 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 designObservational
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

Citations20
Published2019
Admission routes1
Has abstractyes

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