Barriers for kangaroo mother care (KMC) acceptance, and practices in southern Ethiopia: a model for scaling up uptake and adherence using qualitative study
Bibliographic record
Abstract
BACKGROUND: Globally, approximately 15 million babies are born preterm every year. Complications of prematurity are the leading cause of under-five mortality. There is overwhelming evidence from low, middle, and high-income countries supporting kangaroo mother care (KMC) as an effective strategy to prevent mortality in both preterm and low birth weight (LBW) babies. However, implementation and scale-up of KMC remains a challenge, especially in lowincome countries such as Ethiopia. This formative research study, part of a broader KMC implementation project in Southern Ethiopia, aimed to identify the barriers to KMC implementation and to devise a refined model to deliver KMC across the facility to community continuum. METHODS: A formative research study was conducted in Southern Ethiopia using a qualitative explorative approach that involved both health service providers and community members. Twenty-fourin-depth interviewsand 14 focus group discussions were carried out with 144study participants. The study applied a grounded theory approach to identify,examine, analyse and extract emerging themes, and subsequently develop a model for KMC implementation. RESULTS: Barriers to KMC practice included gaps in KMC knowledge, attitude and practices among parents of preterm and LBW babies;socioeconomic, cultural and structural factors; thecommunity's beliefs and valueswith respect to preterm and LBW babies;health professionals' acceptance of KMC as well as their motivation to implement practices; and shortage of supplies in health facilities. CONCLUSIONS: Our study suggests a comprehensive approach with systematic interventions and support at maternal, family, community, facility and health care provider levels. We propose an implementation model that addresses this community to facility continuum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".