A system approach to improving maternal and child health care delivery in Kenya: innovations at the community and primary care facilities (a protocol)
Bibliographic record
Abstract
BACKGROUND: Maternal, fetal and neonatal mortality are higher in low-income compared to high-income countries due to weak health systems including poor access and utilization of health services. Despite enormous recent improvements in maternal, neonatal and under 5 health indicators, more rapid progress is needed to meet the targets including the Development Goal 3(SDG). In Kenya these indicators are still high and comprehensive systems are needed to attain the targets of the SDG 3 by 2030. We describe the structure and methods of a study to assess the impact of an innovative system approach on maternal, neonatal and under-five children outcomes. This will be implemented in two clusters in the Counties of Busia and Bungoma in Kenya. There will be 4 control clusters in Kakamega, UasinGishu, Trans Nzoia and Elgeyo Marakwet Counties in Kenya. The study population will be pregnant women, newborns and under-five children identified over the study period. The objective of the study is to improve access, utilization and quality of Maternal and Child Health care through a predesigned Enhanced Health Care System (EHC) that embodies six WHO pillars of the health system and community owned initiatives including Community Based Organisations and Income Generating Activities. METHODS/DESIGN: A five year quasi-experimental design will be used to compare the outcomes of the implementation of the EHC using the Find Link Treat and retain (FLTR) strategy in one cluster, community owned initiatives in one cluster and four control clusters at baseline and at the end of the study. A Baseline survey will be conducted in year one and an endline in the fifth year in which maternal, neonatal and underfive childhood outcomes will be compared. DISCUSSION: The expected findings from the study include showing trends in improvement in the intervention clusters for morbidity, mortality, health service utilization and access indicators. Use of the health systems approach in health care provision is expected to provide a holistic improvement in the quality of care in the study populations in the intervention clusters that will lead to improved health indicators including morbidity and mortality. It is expected that the findings will inform health policy of the national and county governments in Kenya and worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".