Timing of first prenatal ultrasound and associated factors among women who gave birth at health institutions in Ambo Town, central Ethiopia
Bibliographic record
Abstract
BACKGROUND: The Ethiopian Ministry of Health recommends "one prenatal ultrasound scan before 24 weeks of gestation for every pregnant woman." Despite clear suggestions for timely prenatal ultrasound utilization, little is known about the extent to which it is utilized and the factors affecting the timing of the first prenatal ultrasound examination in the study area. Hence, this study aimed to assess the timing of the first prenatal ultrasound and identify associated factors among women who delivered at health institutions in Ambo town, central Ethiopia. METHODS: This health facility-based cross-sectional study was conducted from September 12 to October 30, 2022. Data were collected through interviews using structured questionnaires and record reviews. A total of 442 participants were recruited through systematic random sampling. Data analysis was performed using a binary logistic regression model in SPSS Version 25. Adjusted odds ratios with a p-value of less than or equal to 0.05 were used to declare statistical significance. RESULTS: Overall, 71% (95%CI: 67.0-75.6) of participants had received a timely prenatal ultrasound. Living in urban areas (AOR = 5.64,95%CI:2.53-12.55), having a history of prenatal ultrasound during previous pregnancy (AOR = 2.47,95%CI:1.24-4.89), attending ANC visits at hospital (AOR = 3.30,95%CI:1.19-9.16), and good knowledge of prenatal ultrasound (AOR = 4.46,95%CI:2.26-8.81) were found to significantly affecting the timing of the first prenatal ultrasound. CONCLUSION: In this study, more than a quarter of the women did not receive timely prenatal ultrasounds. Urban residence, previous use of prenatal ultrasound, attending ANC at the hospital, and having good knowledge were factors identified for timely prenatal ultrasound. Therefore, all stakeholders must work on those identified factors to improve the timely ultrasound scanning.
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
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 | 0.003 |
| 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 | 0.000 |
| 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".