Evaluating quality neonatal care, call Centre service, tele-health and community engagement in reducing newborn morbidity and mortality in Bungoma county, Kenya
Bibliographic record
Abstract
BACKGROUND: Neonatal mortality is a major health burden in Bungoma County with the rate estimated at 31 per 1000 live births and is above the national average of 22 per 1000. Nonetheless, out of the nine sub county hospitals, only two are fairly equipped with necessary infrastructure and skilled personnel to manage neonatal complications such as prematurity, neonatal sepsis, neonatal jaundice, birth asphyxia and respiratory distress syndrome. Additionally, with more than 50% of neonates delivered without skilled attendance, in below par hygiene environments such as home and on the roadsides, with non-existent community based referral system, the situation is made worse. The study aims to evaluate the progress made by an intervention "Collaborative Newborn Support Project" geared towards reducing neonatal mortality rate by 30% between October 2015 and December 2018 in Bungoma County, Kenya. METHODS/DESIGN: This intervention will take a quasi-experimental design approach with experimental and control sites. The project will involve pre- and post-intervention data collection with comparison group to assess intervention effects. The primary outcome will be the percentage reduction of neonatal mortality in Bungoma County. Secondary outcomes include; a) Percentage of mothers or care givers able to identify at least three danger signs in neonates in the project area, b) Proportion of neonates with complications referred to specialized neonatal centers, through the call center, c) Percentage of health providers in neonatal care units who adhere to expected neonatal standards of care (rapid and complete application of standard protocols), d) Percentage increase in neonates with severe complications in the specialized neonatal units and e) Percentage of neonates who stay in neonatal care units beyond 5 days. DISCUSSION: We outline implementation details of the ongoing 'Collaborative Newborn Support Project' in Bungoma County, Kenya. This includes strategies in the operations of the telehealth platform, call centre service, community engagement and measuring of the outputs and outcomes. The funding and ethical approvals have been obtained and the study commenced. TRIAL REGISTRATION: PACTR201712002802638 Retrospectively registered on 5th December 2017 at Pan African Clinical Trials Registry.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".