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Record W3026652339 · doi:10.1186/s12884-020-02978-w

Promoting positive maternal, newborn, and child health behaviors through a group-based health education and microfinance program: a prospective matched cohort study in western Kenya

2020· article· en· W3026652339 on OpenAlexaff
Lauren Y. Maldonado, Julia Songok, John W. Snelgrove, Christian B. Ochieng, Sheilah Chelagat, Justus E. Ikemeri, Monica A. Okwanyi, Donald C. Cole, Laura J. Ruhl, Astrid Christoffersen‐Deb

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of British ColumbiaPublic Health OntarioUniversity of TorontoMount Sinai Hospital
FundersU.S. President’s Emergency Plan for AIDS ReliefUnited States Agency for International Development
KeywordsMedicineReproductive medicineProspective cohort studyPrenatal careCohortLogistic regressionHealth facilityDemographyCohort studyPublic healthFamily medicinePediatricsPregnancyPopulationEnvironmental healthNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Chamas for Change (Chamas) is a group-based health education and microfinance program for pregnant and postpartum women that aims to address inequities contributing to high rates of maternal and infant mortality in rural western Kenya. In this prospective matched cohort study, we evaluated the association between Chamas participation and facility-based delivery. We additionally explored the effect of participation on promoting other positive maternal, newborn and child health (MNCH) behaviors. METHODS: We prospectively compared outcomes between a cohort of Chamas participants and controls matched for age, parity, and prenatal care location. Between October-December 2012, government-sponsored community health volunteers (CHV) recruited pregnant women attending their first antenatal care (ANC) visits at rural health facilities in Busia County to participate in Chamas. Women enrolled in Chamas agreed to attend group-based health education and microfinance sessions for one year; controls received the standard of care. We used descriptive analyses, multivariable logistic regression models, and random effect models to compare outcomes across cohorts 12 months following enrollment, with α set to 0.05. RESULTS: Compared to controls (n = 115), a significantly higher proportion of Chamas participants (n = 211) delivered in a health facility (84.4% vs. 50.4%, p < 0.001), attended at least four ANC visits (64.0% vs. 37.4%, p < 0·001), exclusively breastfed to six months (82.0% vs. 47.0%, p < 0·001), and received a CHV home visit within 48 h postpartum (75.8% vs. 38.3%, p < 0·001). In multivariable models, Chamas participants were over five times as likely as controls to deliver in a health facility (OR 5.49, 95% CI 3.12-9.64, p < 0.001). Though not significant, Chamas participants experienced a lower proportion of stillbirths (0.9% vs. 5.2%), miscarriages (5.2% vs. 7.8%), infant deaths (2.8% vs. 3.4%), and maternal deaths (0.9% vs. 1.7%) compared to controls. CONCLUSIONS: Chamas participation was associated with increased odds of facility-based delivery compared to the standard of care in rural western Kenya. Larger proportions of program participants also practiced other positive MNCH behaviors. Our findings demonstrate Chamas' potential to achieve population-level MNCH benefits; however, a larger study is needed to validate this observed effect. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03188250 (retrospectively registered 31 May 2017).

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)0.001
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 score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.303
Teacher spread0.289 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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