Knowledge on Intrapartum Danger Sign Influences Place of Delivery: The Case of Raya Kobo District, Northeastern Ethiopia
Bibliographic record
Abstract
Background: A skilled birth attendance for every pregnant woman during childbirth is the most crucial intervention for improving maternal health. This study aimed to assess institutional delivery service utilization and associated factors among mothers who gave birth in the last 12 months in Raya Kobo district, Ethiopia. Methods: A community-based cross-sectional study was carried out in the Raya Kobo district of Amhara Regional State during March 2016. Logistic regression analysis was performed to assess the association between each independent variable and the outcome variable. Variables with a p-value <0.05 were considered significant. Results: A total of 493 mothers were included in the study, with a response rate of 95.4%. The mean (+SD) age of the study participants was 29.13 (±6.93) years. About 73% of the study participants had attended at least one antenatal care follow up for their last pregnancy, and 56.6% (95% CI: 52.0, 61.0%) gave birth at health institutions. Travelling for 30 minutes and less [AOR=2.95(1.89, 4.58)], attending antenatal care [AOR=6.0(3.55, 10.13)], having knowledge about intrapartum danger signs [AOR=2.48(1.44, 4.24)] and getting information from health extension workers (HEWs) regarding maternal health services were positively associated. Conclusion: The district health office should strengthen its effort to provide free ambulance accessibility and provide information on danger signs of intrapartum complications and the importance of using institutional delivery service to every mother who came to the antenatal clinic. Furthermore, the district health officials should focus on strengthening the capacity of HEWs in relation to maternal health services.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".