A qualitative study on factors that influence women’s choice of delivery in health facilities in Addis Ababa, Ethiopia
Bibliographic record
Abstract
BACKGROUND: Facility based delivery for mothers is one of the proven interventions to reduce maternal and neonatal morbidity and mortality. This study identified women's reasons for seeking to give birth in a health facility and captured their perceptions of the quality of care they received during their most recent birth, in a population with high utilization of facility based deliveries. METHODS: This qualitative study was conducted in eight health centers in Addis Ababa. Women bringing their index child for first vaccinations were invited to participate in an in-depth interview about their last delivery. Sixteen in-depth interviews were conducted. Interviews were conducted by trained researchers using a semi-structured interview guide. The data were transcribed verbatim in Amharic and translated into English. A thematic analysis was conducted to answer specific study questions. RESULTS: All research participants expressed a preference for facility based delivery because of their awareness of obstetric complications, and related perceptions that facility-birth is safer for the mother and child. Dimensions of quality of care and the cost of services were identified as influencing decisions about whether to seek care in the public or private sector. Media campaigns, information from social networks and women's experiences with healthcare providers and facilities influenced care-seeking decisions. CONCLUSIONS: The universal preference for facility-based birth by women in this study indicates that, in Addis Ababa, facility based delivery has become a preferred norm. Sources of information for decision-making and the dimensions of quality prioritized by women should be taken into account to develop interventions to promote facility-based births in other settings.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".