Advancing Antenatal Care in Ethiopia: The Impact of Tele-Ultrasound on Antenatal Ultrasound Access in Rural Ethiopia
Bibliographic record
Abstract
Introduction: Access to antenatal ultrasound is limited in low-income countries such as Ethiopia. Virtual care platforms that facilitate supervision and mentoring for ultrasound scanning may improve patient access by facilitating task-sharing of antenatal ultrasound with midlevel providers. The purpose of this study was to assess the feasibility of a large volume tele-ultrasound program in Ethiopia, its impact on antenatal care (ANC) and patient access, and its sustainability as it transitioned from a pilot project to a continuing clinical program. Methods: Health care providers at two health centers in the North Shoa Zone, Ethiopia, performed antenatal tele-ultrasound exams with remote guidance from obstetricians located in urban areas. Data regarding ANC and ultrasound utilization, participant travel, ultrasound findings, specialist referrals, and participant experience were collected through a mobile app. Results: Between November 2020 and December 2023, 7,297 tele-ultrasound exams were performed. Of these, 489 tele-ultrasound exams were performed during the period of data collection from October to December 2022. The availability of tele-ultrasound at the two health centers significantly reduced participant travel distance (4.2 km vs. 10.2 km; p < 0.01; one-way distance). Most participants (99.2%) indicated the tele-ultrasound service was very important or important, with high levels of satisfaction. Clinically significant findings were identified in 26 cases (5.3%), leading to necessary referrals. Conclusion: This study demonstrated the feasibility of a large volume tele-ultrasound program in Ethiopia, its impact on improving the quality of ANC, and its sustainability. These findings lay a foundation upon which low-income countries can develop tele-ultrasound programs to improve antenatal ultrasound access.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".