Scaling Up Kangaroo Mother Care Through a Facility Delivery Model in Rural Districts of Pakistan: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The neonatal mortality rate in Pakistan is the third highest in Asia, with 8.6 million preterm babies. These newborns require warmth, nutrition, and infection protection, typically provided by incubators. However, the high maintenance and repair costs of incubators pose a barrier to accessibility for many premature and low birth weight neonates in low- and middle-income countries. This study aims to implement a context-specific kangaroo mother care (KMC) model in Sanghar within secondary health care facilities and catchment communities. OBJECTIVE: This study aims to achieve at least 80% KMC coverage for premature and low birth weight neonates. METHODS: This research uses a mixed methods design grounded in implementation science principles, with the goal of developing adaptive strategies tailored to district and facility managers, as well as health care workers, leveraging previous evidence on the benefits of KMC. The research is conducted in the district of Sanghar, Sindh with an emphasis on promoting KMC for infants weighing between 1200 and 2500 g in three facilities. It includes preimplementation data collection, training of health care providers and lady health workers, and intervention involving mother-baby skin-to-skin contact, breastfeeding initiation, and postdischarge follow-ups. Ethical considerations and data management are prioritized, to improve KMC coverage and neonatal health outcomes. RESULTS: This research will be implemented over a period of 18 months. The primary objective of this research is to achieve an 80% improvement in KMC coverage, with the secondary objective to promote optimal breastfeeding practices among postpartum mothers. Key indicators include the proportion of eligible infants enrolled in KMC, the percentage of mother-baby pairs receiving skin-to-skin care postdischarge, and the duration of KMC during the neonatal period. Additionally, the study will assess exclusive breastfeeding rates, neonatal weight gain, and neonatal deaths within the cohort. The data management team will evaluate the effectiveness of the model in achieving the targeted KMC coverage. CONCLUSIONS: The integration of KMC into the health care system will provide valuable insights for policy makers regarding effective implementation and scaling strategies. The study's findings will highlight facilitators and barriers to KMC adoption, benefiting regions across Pakistan and globally. Additionally, these findings will offer valuable insights for the development of future newborn care programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56142.
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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 | 0.051 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.009 |
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".