Factors associated with maternity waiting home use among women in Jimma Zone, Ethiopia: a multilevel cross-sectional analysis
Bibliographic record
Abstract
OBJECTIVE: To identify individual-, household- and community-level factors associated with maternity waiting home (MWH) use in Ethiopia. DESIGN: Cross-sectional analysis of baseline household survey data from an ongoing cluster-randomised controlled trial using multilevel analyses. SETTING: Twenty-four rural primary care facility catchment areas in Jimma Zone, Ethiopia. PARTICIPANTS: 3784 women who had a pregnancy outcome (live birth, stillbirth, spontaneous/induced abortion) 12 months prior to September 2016. OUTCOME MEASURE: The primary outcome was self-reported MWH use for any pregnancy; hypothesised factors associated with MWH use included woman's education, woman's occupation, household wealth, involvement in health-related decision-making, companion support, travel time to health facility and community-levels of institutional births. RESULTS: Overall, 7% of women reported past MWH use. Housewives (OR: 1.74, 95% CI 1.20 to 2.52), women with companions for facility visits (OR: 2.15, 95% CI 1.44 to 3.23), wealthier households (fourth vs first quintile OR: 3.20, 95% CI 1.93 to 5.33) and those with no health facility nearby or living >30 min from a health facility (OR: 2.37, 95% CI 1.80 to 3.13) had significantly higher odds of MWH use. Education, decision-making autonomy and community-level institutional births were not significantly associated with MWH use. CONCLUSIONS: Utilisation inequities exist; women with less wealth and companion support experienced more difficulties in accessing MWHs. Short duration of stay and failure to consider MWH as part of birth preparedness planning suggests local referral and promotion practices need investigation to ensure that women who would benefit the most are linked to MWH services.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".