Evaluation of an educational program for essential newborn care in resource-limited settings: Essential Care for Every Baby
Bibliographic record
Abstract
BACKGROUND: Essential Care for Every Baby (ECEB) is an evidence-based educational program designed to increase cognitive knowledge and develop skills of health care professionals in essential newborn care in low-resource areas. The course focuses on the immediate care of the newborn after birth and during the first day or until discharge from the health facility. This study assessed the overall design of the course; the ability of facilitators to teach the course; and the knowledge and skills acquired by the learners. METHODS: Testing occurred at 2 global sites. Data from a facilitator evaluation survey, a learner satisfaction survey, a multiple choice question (MCQ) examination, performance on two objective structured clinical evaluations (OSCE), and pre- and post-course confidence assessments were analyzed using descriptive statistics. Pre-post course differences were examined. Comments on the evaluation form and post-course group discussions were analyzed to identify potential program improvements. RESULTS: Using ECEB course material, master trainers taught 12 facilitators in India and 11 in Kenya who subsequently taught 62 providers of newborn care in India and 64 in Kenya. Facilitators and learners were satisfied with their ability to teach and learn from the program. Confidence (3.5 to 5) and MCQ scores (India: pre 19.4, post 24.8; Kenya: pre 20.8, post 25.0) improved (p < 0.001). Most participants demonstrated satisfactory skills on the OSCEs. Qualitative data suggested the course was effective, but also identified areas for course improvement. These included additional time for hands-on practice, including practice in a clinical setting, the addition of video learning aids and the adaptation of content to conform to locally recommended practices. CONCLUSION: ECEB program was highly acceptable, demonstrated improved confidence, improved knowledge and developed skills. ECEB may improve newborn care in low resource settings if it is part of an overall implementation plan that addresses local needs and serves to further strengthen health systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".