Mothers’ perceptions of the practice of kangaroo mother care for preterm neonates in sub-Saharan Africa: a qualitative systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to explore the experiences of mothers with the practice of kangaroo mother care (KMC) for preterm neonates at home in sub-Saharan Africa. INTRODUCTION: About 7000 newborn babies die every day around the world. About 80% of these deaths occur in sub-Saharan Africa and southern Asia. Preterm birth and low birth weight (LBW) are major causes of newborn deaths in these regions. Kangaroo mother care is an alternative way to care for LBW preterm neonates; however, the rate of practice remains low. Studies have identified a range of barriers, primarily at the healthcare system level, but there is a dearth of evidence on the factors and enablers at the community level. INCLUSION CRITERIA: The review will consider studies conducted in sub-Saharan Africa on the perceptions and experiences of mothers who have given birth to preterm babies and have practiced KMC at home. Qualitative studies in English and French conducted from January 1979 to the present that exclusively use qualitative research methods including, but not limited to, phenomenology, grounded theory, ethnography, action research and feminist research will be included. METHODS: PubMed, Embase, Web of Science, Scopus, African Index Medicus (AIM), Academic Search Complete, CINAHL complete, Education Source and Health source: Nursing/Academic Edition will be searched. Eligible studies will be critically appraised using the standardized Joanna Briggs Institute tool. Findings will be pooled using the meta-aggregative approach, and confidence will be assessed according to the ConQual approach.
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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 | 0.076 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".