Effect of health systems strengthening in influencing maternal and neonatal health outcomes in Bungoma County, Kenya
Bibliographic record
Abstract
Introduction: maternal and neonatal health status indicators have steadily improved over time in Kenya. Significant challenges remain, including persistent inequities between population subgroups, and a health system that delivers variable quality care and inconsistent access to care. This paper highlights results of an ex-post evaluation to assess the impact of maternal and health systems strengthening intervention to improve newborn health outcomes in Bungoma County, Kenya, focusing on access to and quality of maternal and neonatal care. Methods: the study design was quasi-experimental, using household surveys to assess outcomes at baseline and end-line. Stratified cluster sampling was used to identify households, based on heath facility catchment areas. Inclusion criteria were women aged 18-49. Chi-square and fisher´s exact tests were used. Patched-up design was used to compare outcomes before and after the intervention and intervention and control sub-counties. Results: provision of transport vouchers decreased barriers to accessto health care, resulting in an increased number of deliveries in health facilities. Women in the end-line group were 95% more likely to deliver at a health facility compared to 74% at baseline. The intervention improved potential and effective access to antenatal care as well as deliveries in health facilities. This positively impacted quality of care provision in the sub-counties. Conclusion: key elements of health system strengthening included reducing cost barriers and enhancing the capacity of the health facilities to deliver high quality care. The intervention addressed commonly identified supply-and demand-side barriers, providing stronger evidence that addressing these hindrances would improve utilization of maternal and child health services.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".