Health facilities preparedness to deliver maternal and newborn health care in Kilifi and Kisii Counties, Kenya
Bibliographic record
Abstract
Abstract Introduction: Health facility preparedness to deliver quality maternal and newborn care reduces maternal and newborn morbidity and mortality by avoiding the three delays (delay in deciding to seek care from a skilled attendant by pregnant woman; delay in reaching the facility with capacity to offer basic emergency obstetric care; and delay in receiving emergency care upon reaching a health facility). Rapid assessment and review of previous health records has shown that 16 health facilities in rural Kenya had poor maternal and newborn indicators. As a result, support was given to these facilities by providing basic emergency obstetric and newborn care (BEmONC) and comprehensive emergency obstetric and newborn care (CEmONC) training to providers, provision of equipment and supplies, and strengthening referral linkages. This study described the preparedness of the facilities to deliver maternal and newborn health care services at the end of the project implementation. Methods: A descriptive cross-sectional study was conducted in targeted rural counties of Kilifi and Kisii counties in December 2019 covering 16 Government of Kenya (GoK) health facilities to describe the preparedness of the facilities to deliver maternal and newborn healthcare services by examining the availability of drugs, commodities, equipment, staffing, general requirements (water and electricity, and guidelines), and the ability to perform. The results of the assessment are described using frequency and percentages, and comparative synthesis. Results: All of the 16 facilities were offering routine ANC and normal vaginal delivery services, however only two were providing CEmONC services. Most of the essential medicines and commodities were available in most of the health facilities as well as the required equipment. BEmONC and CEmONC guidelines were available in Kilifi health facilities and none in Kisii. There was only one staff in each county available 24/7 for Caesarian Section (CS) and only one anesthetist available in Kilifi. Electricity was available in all the facilities, however only half had secondary power supply. All the facilities offering CS were equipped with generators as a secondary power back-up. Conclusion: The health facilities reported availability of most of the drugs, commodities, and equipment than on general requirements as per their level of operation, however staffing and guidelines were limited. Facilities in Kilifi performed better than in Kisii. To deliver quality maternal and newborn health services, more support is required towards general infrastructure and human resources. Continuous monitoring of these services will help in the allocation of resources based on the need of the health facilities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".