Midwives' perspectives on rural birthing experiences and newborn survival in Ghana
Bibliographic record
Abstract
Access to essential and emergency newborn care services remains a challenge in low- and middle-income countries (LMICs), especially in rural and remote areas where various factors increase maternal and newborn vulnerability. The scarcity of midwives on a global scale further strains obstetric and neonatal services, as midwives work at the forefront in many LMICs. In Ghana, neonatal deaths at birth and within 24 h contribute significantly to infant mortality rate, with midwives caring as frontline health workers. However, there is limited exploration of midwives' experiences in managing these situations. This study aimed to unveil the meanings and articulate the experiences of midwives who face newborns with respiratory distress at birth in rural southern Ghana. Interpretive Phenomenological approach was used to explore thirteen (13) midwives’ experiences of managing newborns in respiratory distress in rural birth settings amidst scare life saving resources, skilled staff shortage and limited advanced health care. The midwives were purposively sampled from the Shai-Osudoku district and data were collected through face-to-face interviews. Content analysis was conducted on the interview transcripts and rich narratives developed in the research report. Rural midwives have limited access to newborn lifesaving equipment, adequate training, skill staff support, and timely advance care for newborns. These issues are driving factors for newborn referral in rural health care, but transportation gaps impede timely advance care delivery. Rural midwives’ inadequate psychosocial support and lack of enabling work environment engender moral distress with emotional burden requiring sustained attention from national leadership, as well as midwifery professional networks and regulatory bodies. Newly qualified midwives require sustainable support as they struggle in birth spaces where ethical questions emerge as family members are engaged as support persons in neonatal resuscitation. Future research is needed to investigate Chiefs, Queen Mothers and Community Elders’ engagement in community-based interventions to support timely access to quality care and midwives’ heroic practices of saving maternal/newborn lives in rural Ghana to help achieve SDG 3.2.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".