Why do newborns die? Perspectives of community members and Health care providers in the Lawra Municipality in Upper West Region, Ghana
Bibliographic record
Abstract
Abstract BACKGROUND Neonatal deaths contribute significantly to under-five deaths and are a crucial indicator of a country's socioeconomic development, quality of life, and health status. Neonatal deaths occur both at home and in health facilities. Hence community members' and health workers' perspectives collectively define neonatal survival. AIM This study explored the perceived causes of neonatal deaths among health service providers and community members. METHODS This study employed a qualitative descriptive design with a purposive sampling method of 30 participants (18 community members and 12 healthcare providers). Data were collected using Focus Group Discussion (FGD), transcribed verbatim, coded and analysed using the content analysis technique. RESULTS Three categories emerged to describe the factors perceived to cause newborn deaths at health facilities and in the community. These categories were: (1) human factors (newborns, their mothers/families and health staff); (2) place factors (logistics, equipment and their interaction with newborns); and (3) time factors (delays, transport and referral). CONCLUSIONS The study identified differences in perspectives between health workers and community members. These differences can affect interventions in neonatal care. Health authorities are encouraged to engage their communities with geographic-specific factors causing neonatal deaths to identify and understand the influencing factors and take everyday actions towards reducing neonatal deaths.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".