Exploring Perceptions about Enablers of Women’s Attendance and Adherence to the Recommended Antenatal Care Visits in Rwanda: A Qualitative Study
Bibliographic record
Abstract
Background: Antenatal care (ANC) helps ensure the best health conditions of the mother and foetus during pregnancy. However, achieving optimal ANC attendance and adherence to the World Health Organization (WHO) recommendations remains a global challenge, with significant disparities in attendance rates. A qualitative study was conducted exploring pregnant women's perspectives of various enablers to their attendance and adherence to recommended ANC visits in Rwanda. Methods: This exploratory qualitative study involved 22 pregnant women attending ANC in four public health centres in the Eastern province, of Rwanda. An interview guide with semi-structured questions was used to gather information about the moderators of ANC attendance and adherence among pregnant women. Data were audio-recorded and transcribed verbatim, and thematic analysis was used to categorize themes under the five-level Social Ecological Model (SEM). Results: Early recognition of pregnancy, financial stability, and female participation in decision-making were identified as intrapersonal enabling factors of ANC attendance and adherence; spousal support was identified as an interpersonal enabling factor; community health workers, and community relationships as community enabling factors; availability and cost of ANC services as institutional enabling factors; and media campaign, community outreach as public policy enabling factors contributing to the pregnant women's attendance and adherence to ANC visits. Conclusion: Enablers at multiple levels affect women's attendance and adherence to ANC visits. It is essential to consider each level when implementing effective strategies to maximize ANC attendance and adherence to the WHO recommendations in order to improve maternal-foetal well-being in Rwanda.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".