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Record W3057754066 · doi:10.1093/pch/pxaa068.064

65 Mama na Mtoto: Health Outcome Achievements Following Implementation of Comprehensive Maternal Newborn Programming in Rural Tanzania

2020· article· en· W3057754066 on OpenAlexaffabout
Jennifer L. Brenner, Dismas Matovelo, Boniphace Maendaelo, Wemaeli Mweteni, Nalini Singhal, Alberto Nettel‐Aguirre, Leonard Subi

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHealth facilityTanzaniaMedicineEnvironmental healthService delivery frameworkPsychological interventionGovernment (linguistics)Health carePopulationNursingService (business)SocioeconomicsBusinessHealth servicesEconomic growth

Abstract

fetched live from OpenAlex

Abstract Introduction/Background Preventable deaths in pregnant women and newborns remain unacceptably high in East Africa. Limited antenatal, delivery and postnatal care-seeking combined with service delivery gaps at government facilities contribute to high mortality. Between 2016-2019, partners from Tanzania, Uganda, and Canada jointly developed, implemented, and evaluated a comprehensive, district-wide maternal, newborn, and child health (MNCH) ‘package’ in Lake Zone, Tanzania. Known locally as ‘Mama na Mtoto’, the scale-up programming involved training and capacity building for district managers, health facility staff and a network of volunteer community health workers selected by their own communities. Objectives To quantitatively assess changes in MNCH health outcomes following the Mama na Mtoto intervention. Design/Methods MNCH household-level care-seeking outcomes were assessed using a pre/post coverage survey adapted from the Demographic Health Survey. Households and women (15-49 years), selected through cluster sampling (cluster unit=hamlet), were surveyed by local, trained research assistants using tablet-based surveys. MNCH service outcomes were assessed at all government health facilities using a comprehensive pre/post cross-sectional audit tool; key measures included staff, equipment, infrastructure, supplies, and medication availability. Descriptive statistics for antenatal care (ANC), health facility delivery (HFD), and postnatal care (PNC)-related indicators were analyzed pre- and post-intervention using R software. Composite health facility ‘Readiness Scores’ were calculated through tallies of relevant itemized facility-based measures for each core MNCH service area across the district. Absolute percentage differences, confidence intervals and design effect are presented where relevant. Results In total, 1,977 households, 2,438 women, and 45 health facilities were surveyed pre-intervention and 1,835 homes, 2,073 women, and 49 health facilities were surveyed post. Care-seeking indicators with statistically significant changes were ANC 4+ (+11%), ANC <12 weeks (+7%), HFD (+17%), and PNC for mothers (+9%); PNC for babies was not significant. Increases in composite MNCH Service Readiness Scores were as follows: ANC +24%, essential newborn care +42%, newborn resuscitation +37%, and labour and delivery +27%. Conclusion The comprehensive MnM package was associated with important improvements in the demand (care-seeking) and service (facility readiness) health outcomes. Attribution is complicated by an uncontrolled health system and lack of district controls; however, the extensive scope, reach, and positive changes are promising and consistent with sustained Ugandan experiences. Best practice documentation is critical to facilitate scale-up and progress acceleration of MNCH programs in Tanzanian and East African 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)0.001
metaresearch head score (Gemma)0.002
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.340
Teacher spread0.316 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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