Development and evaluation of a kangaroo mother care implementation model in South Ethiopia
Bibliographic record
Abstract
AIM: To develop a model for increasing the coverage of kangaroo mother care (KMC), which involved ≥8 h of skin-to-skin contact per day and exclusive breastfeeding, for small babies with birth weight < 2000 g in South Ethiopia. METHODS: A mixed methods study was conducted between June 2017 and January 2019 at four hospitals and their catchment areas. Iterative cycles of implementation, program learning and evaluation were used to optimise KMC implementation models. The study explored the community-facility continuum of care and assessed the proportion of neonates with a birth weight less than 2000 g receiving effective KMC. RESULTS: Three KMC implementation models were tested with Model 2 being the final version. This model included enhanced identification of home births, improved referral linkages, immediate skin-to-skin care initiation in facilities and early contact after discharge. These improvements resulted in 86% coverage of effective facility-based KMC initiation for eligible babies. The coverage was 81.5% at discharge and 57.5% 7 days after discharge. The mean age of babies at KMC initiation was 8.2 days (SD = 5.7). CONCLUSION: The study found that the KMC implementation model was feasible and can lead to substantial population-level KMC coverage for small babies.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".