Maternity waiting areas – serving all women? Barriers and enablers of an equity-oriented maternal health intervention in Jimma Zone, Ethiopia
Bibliographic record
Abstract
In Ethiopia, maternal waiting areas (MWAs) - residential areas near health facilities where women can stay while waiting to give birth - are community-based, equity-oriented interventions to improve maternal outcomes among rural populations. In this qualitative study we sought to explore the barriers and enablers that Health Extension Workers (HEWs) encounter when engaging with communities about MWAs. We conducted semi-structured interviews with HEWs across rural sites in Jimma Zone, Ethiopia. Drawing from an ecological model of social determinants of maternal and child health, we analysed data using thematic coding methods. HEWs reported a variety of factors that determined MWA use, including the number of children at home, previous childbirth experiences, community support networks, decision making practices within families, the availability and acceptability of health services, geographical access, and health beliefs. HEWs worked to increase the use of MWAs by engaging with husbands and communities, raising awareness in target groups of women, and managing community participation. Policies and practices that support enhanced training for HEWs, increased resources for communities, and greater opportunities for HEWs to liaise with decision makers at various levels of influence are possible ways forward to improve MWA use, specifically, and maternal and neonatal/child health outcomes more generally.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".