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Record W4361985561 · doi:10.2196/42041

Intervention in Mothers and Newborns to Reduce Maternal and Perinatal Mortality in 3 Provinces in South Africa Using a Quality Improvement Approach: Protocol for a Mixed Method Type 2 Hybrid Evaluation

2023· article· en· W4361985561 on OpenAlexvenueno aff
Terusha Chetty, Yages Singh, Willem Odendaal, Solange Mianda, Nada Abdelatif, Samuel Manda, Helen Schneider, Ameena Goga

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsnot available
FundersMedical Research CouncilUniversity of PretoriaSouth African Medical Research Council
KeywordsMedicinePandemicIntervention (counseling)Health facilityHealth careProtocol (science)Mortality rateData collectionDocumentationChild mortalityEnvironmental healthFamily medicineMedical emergencyNursingPopulationCoronavirus disease 2019 (COVID-19)DiseaseAlternative medicine

Abstract

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BACKGROUND: The COVID-19 pandemic undermined gains in reducing maternal and perinatal mortality in South Africa. The Mphatlalatsane Initiative is a health system intervention to reduce mortality and morbidity in women and newborns to desired levels. OBJECTIVE: Our evaluation aims to determine the effect of various exposures, including the COVID-19 pandemic, and a system-level, complex, patient-centered quality improvement (QI) intervention (the Mphatlalatsane Initiative) on maternal and neonatal health services at 21 selected South African facilities. The objectives are to determine whether Mphatlalatsane reduces the institutional maternal mortality ratio, neonatal mortality rate, and stillbirth rate (objective 1) and improves patients' experiences (objective 2) and quality of care (objective 3). Objective 4 assesses the contextual and implementation process factors, including the COVID-19 pandemic, that shape Mphatlalatsane uptake and variation. METHODS: This study is an implementation science type 2 hybrid effectiveness, controlled before-and-after design with quantitative and qualitative components. The Mphatlalatsane intervention commenced at the end of 2019. For objective 1, intervention and control facility-level data from the District Health Information System are compared for changes in institutional maternal and neonatal mortality and stillbirth rates and associations with QI, the COVID-19 pandemic, and both. This first analysis includes data from 18 facilities, regardless of their allocation to intervention or comparison, to obtain a general idea of the effect of the COVID-19 pandemic. For objectives 2 to 3, data collectors abstract data from maternal and neonatal records, interview participants, and conduct neonatal facility assessments. For objective 4, interviews, program documentation, surveys, and observations are used to assess how contextual factors at the macro-, meso-, and microlevels explain variation in intervention uptake and outcome. The intervention dose is measured at the microlevel only in the intervention facilities. The study assesses the Mphatlalatsane Initiative from 2020 to 2022. RESULTS: From preliminary analysis, across the 3 provinces, maternal and neonatal deaths increased during the COVID-19 pandemic, whereas stillbirths remained unchanged. Maternal satisfaction with quality of care was >90%. The COVID-19 pandemic severely disrupted the QI teams functioning. However, the QI teams regained their pre-COVID-19 momentum by adapting the QI model, with advisers providing mentoring and support. Variation in adoption at the mesolevel was related to stable and motivated leadership (particularly at the facility level), poor integration into routine processes, and buy-in from senior district managers who were affected by competing priorities. Varying referral and specialist outreach systems, staff availability and development, and service delivery infrastructure are plausible factors in variable outcomes. CONCLUSIONS: Few evaluations rigorously evaluated the effect of health system interventions on improving health services and outcomes. Results will inform the scaling up of successful intervention components and strategies to mitigate the effects of the COVID-19 pandemic or similar emerging epidemics on maternal and neonatal mortality. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42041.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.455
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.535
GPT teacher head0.647
Teacher spread0.113 · 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 teacher head, not a consensus.

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

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

Citations12
Published2023
Admission routes1
Has abstractyes

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