Improving maternal and child health outcomes through a community involvement strategy in Kabula location, Bungoma County, Kenya
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 five children health indicators, more rapid progress is needed to meet the targets including the Sustainable Development Goal 3(SDG). In Kenya these indicators are still high and comprehensive systems are needed to attain these goals. Objective: To facilitate innovative partnerships in health care provision and to assess trends in access, utilization and quality of Maternal and Child Health care through the health systems approach using community owned initiatives including use of community owned resourse persons (CORPs), establishment of Community Based Organisations (CBOs) and Income Generating Activities(IGAs). Study site: This was implemented in Kabula location, Bungoma County, Kenya between January 2016 and April 2019. Study population: Pregnant women, newborns and under-five children living in Kabula location identified by Community Owned Resource Persons (CORPs). Methods: A prospective study to show trends in maternal, neonatal and infant outcomes through the implementation of community owned initiatives. Findings: General, under five and antenatal clinic attendance increased four fold in 2016,2017 and 2018. There was a 76% full immunization coverage with 97% BCG and 84% Polio coverage respectively among children studied. There was an 87% facility delivery rate among the pregnant women enrolled in the study. Conclusions: Trends in Maternal and under-five health indicators in Kabula showed improvements over the study period following the implementation of the community owned initiatives and community participation. Recommendations: The community owned initiatives as implemented in this study is useful in primary care and universal health coverage programs in health care delivery systems in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".