Antenatal Doppler ultrasound implementation in a rural sub-Saharan African setting: exploring the perspectives of women and healthcare providers
Bibliographic record
Abstract
BACKGROUND: The World Health Organization recommends research to evaluate the effects of a single third trimester Doppler ultrasound examination on preventable deaths in unselected-risk pregnancies, particularly in low- and middle-income countries (LMICs) where the evidence base is scarce. While evaluating such technologies, researchers often ignore women and health care provider perspectives. This study explored the views and experiences of women and healthcare providers regarding the use of advanced ultrasound technology to optimize the health of mothers and their babies in a rural community in mid-western Uganda. METHODS: We enrolled 53 mothers and 10 healthcare providers, and captured data on their perceptions, barriers, and facilitators to the use of Doppler ultrasound technology using focus group discussions, semi-structured interviews and observations. Using qualitative content analysis, we inductively coded the transcripts in ATLAS.ti 8.0, detecting emerging themes. RESULTS: Women were afraid that ultrasound would harm them or their fetuses and many of them had never seen an ultrasound scan. The majority of the women found their partners supportive to attend antenatal care and use ultrasound services. Healthcare providers in Kagadi Hospital were unfamiliar with Doppler technology and using it to guide clinical decisions. Other barriers to the implementation of Doppler ultrasound included shortage of trained local staff, insufficient equipment, long distance to and from the hospital, and frequent power cuts. CONCLUSIONS: We found limited exposure to Doppler ultrasound technology among women and healthcare providers in mid-western Uganda. Engaging male partners may potentially influence the likelihood of accepting and using it to improve the health of women and their fetuses while wide spread myths and misconceptions about it may be changed by community engagement. Healthcare workers experienced difficulties in offering follow-up care to mothers detected with complications and Doppler ultrasound required a high level of training. While introducing advanced ultrasound machines to weak health systems, it is important to adequately train healthcare providers to avoid inappropriate interventions based on misinterpretation of the findings, consider where it is likely to be most beneficial, and embed it with realistic clinical practice guidelines.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".