Barriers and enablers to emergency obstetric and newborn care services use in Wolaita Zone, Southern Ethiopia: a qualitative case study
Bibliographic record
Abstract
BACKGROUND: Globally, 11.4 million untreated obstetric complications did not receive Emergency Obstetric and Newborn Care (EmONC) services yearly, with the highest burden in low and middle-income countries. Half of the Ethiopian women with obstetric complications did not receive EmONC services. However, essential aspects of the problem have not been assessed in depth. This study, therefore, explored the various aspects of barriers and enablers to women's EmONC services utilization in southern Ethiopia. METHODOLOGY: A qualitative case study research design was used in nine districts of the Wolaita Zone. A total of 37 study participants were selected using a purposive stratified sampling technique and interviewed till data saturation. Twenty-two key informant interviews were conducted among front-line EmONC service providers, managers, community leaders, and traditional birth attendants (TBAs). Individual in-depth interviews were conducted among 15 women with obstetric complications. The trustworthiness of the research was assured by establishing credibility, transferability, conformability, and dependability. NVivo 12 was used to assist with the thematic data analysis. RESULT: Five themes emerged from the analysis: service users' perception and experience (knowledge, perceived quality, reputation, respectful care, and gender); community-related factors (misconceptions, traditional practices, family and peer influence, and traditional birth attendants' role); access and availability of services (infrastructure and transportation); healthcare financing (drugs and supplies, out-of-pocket expenses, and fee exemption); and health facility-related factors (competency, referral system, waiting time, and leadership). CONCLUSION: Many women and their newborns in the study area suffered severe and life-threatening complications because of the non-utilization or delayed utilization of EmONC services. A key policy priority should be given to enhancing women's awareness, eliminating misconceptions, improving women's autonomy, and ensuring traditional practices' role in EmONC service utilization. Community awareness interventions are required to enhance service uptake. Furthermore, the health systems must emphasize improving the quality of care, inequitable distribution of EmONC facilities, and essential drugs. The financial constraints need to be addressed to motivate women from low socioeconomic status. Furthermore, intersectoral collaboration is required to maintain a legal framework to control and prohibit home deliveries and empower women.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".