Diverse Parental Experiences of Kangaroo Care in Neonatal Units Across Healthcare Systems: A Meta‐Synthesis
Bibliographic record
Abstract
BACKGROUND: Kangaroo Care is an effective practice recommended by WHO for newborns, especially preterm infants, to reduce mortality and morbidity and improve health outcomes. Understanding parents' experiences with Kangaroo Care is vital as it can significantly influence uptake and sustained practice; however, experiences may vary across healthcare systems. AIM: To explore parents' experiences of Kangaroo Care in neonatal units and to examine differences across international health systems. DESIGN: A qualitative meta-synthesis. REVIEW METHODS: A systematic search of the literature was carried out over seven databases, including CINAHL, MEDLINE ALL, EMBASE, PsycINFO, Maternity & Infant Care, Scopus and Cochrane Library. Qualitative studies published in English from 2010 to January 2024 were included. Data extraction and quality appraisal, using the CASP Qualitative Checklist, were undertaken. Meta-synthesis of the included qualitative findings was carried out. The findings were reported following the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) guideline. The protocol was registered on PROSPERO (CRD42023483347). RESULTS: Twenty-five studies were included and four themes were identified: parental fulfilment from Kangaroo Care, Hardship in Kangaroo Care practice, Roadblocks and difficulties in adopting and Building bridges to encourage and support Kangaroo Care. CONCLUSION: This review underscores the multifaceted nature of parental experiences, including positive and challenging aspects, as well as significant barriers and facilitators that influenced Kangaroo Care implementation. By understanding these experiences and factors that hinder and enable, healthcare systems and professionals can better support and empower parents to improve the effectiveness of Kangaroo Care. IMPACT AND IMPLICATIONS: Kangaroo Care is lifesaving, particularly in low-income countries, but can be a challenge for parents providing it. By addressing deficiencies in infrastructure and resources, barriers can be minimised, thereby encouraging the practice of Kangaroo Care. This is especially important in lower-middle- and low-income countries where the practice is most effective and the practice is lowest. PATIENT OR PUBLIC CONTRIBUTION: This project is a meta-synthesis; therefore, no patient or public contribution was deemed necessary.
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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 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".