Post-War Assessment of the Prevalence and Determinants of Unplanned Pregnancy Among Pregnant Women Attending Public Hospitals in Conflict-Zones of South Wollo, Northeast Ethiopia, 2022, Multi-Center Study
Bibliographic record
Abstract
Besfat Berihun Erega,1 Wassie Yazie Ferede,1 Fillorenes Ayalew Sisay,1 Eyaya Habtie Dagnaw,1 Abeba Belay Ayaliew,1 Wondimnew Gashaw Ketema,2 Eyob Shite Lake3 1Department of Midwifery, College of Medicine and Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia; 2Department of Midwifery, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia; 3Department of Midwifery, College of Medicine and Health Sciences, Woldia University, Woldia, EthiopiaCorrespondence: Besfat Berihun Erega, Email berihunbesfat@gmail.comBackground: Worldwide, although unplanned pregnancy seems decreasing, 38% of pregnancies are unintended. In sub-Saharan Africa, unintended pregnancy accounts for more than a quarter of the 40 million pregnancies that occur annually.Objective: The study aimed to assess the prevalence and determinants of unplanned pregnancy among pregnant women attending public hospitals in conflict-zones of South Wollo zone, Northeast Ethiopia, 2022.Methods: A multi-center facility-based cross-sectional study was conducted from December 20, 2021 to February 30, 2022 among the hospitals of South Wollo zone that that were destructed by the armed conflict.Results: The prevalence of unplanned pregnancy in our study was 44.72% (CI=39.40– 48.08%).Conclusion: In this study nearly half of the pregnancies were unplanned, which is shocking to the health care system in the 21st century.Keywords: unplanned pregnancy, armed conflict, post-war assessment, Ethiopia
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".