‘We truly feel limited’: nurses and midwives’ perspectives on multi-level factors influencing women’s adherence to ANC in Rwanda: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Antenatal care (ANC) is essential for improving maternal and neonatal health outcomes, yet adherence to ANC services remains a challenge in many low-income settings, including Rwanda. Understanding nurses and midwives' perspectives on factors influencing ANC adherence is crucial for developing targeted interventions to enhance service utilization and maternal health outcomes. METHODS: This study employed a qualitative descriptive design to explore the perspectives of nurses and midwives on ANC adherence in Rwanda. Fifteen in-depth interviews (IDIs) were conducted using a semi-structured interview guide in Kinyarwanda. The interviews were verbatim transcribed and then translated into English. Atlas.ti 7 software was used to organise the data and then thematically analysed. RESULTS: The perspectives of nurses and midwives were summarised in four themes. Participants mentioned facilitators of ANC engagement with ANC services such as community education, structural motivators, availability of diagnostic infrastructure like ultrasound, and nurses and midwives training and mentorship. The barriers to women's ANC adherence noted by participants are cultural beliefs and community misconceptions, stigma and secrecy surrounding unintended pregnancies, cost-related delays in ANC seeking, gender dynamics and relationships. Nurses and midwives also highlighted health care system constraints, such as staffing shortages and infrastructure and equipment limitations. Recommended interventions to enhance ANC adherence included community engagement and support, increased staff and resources, and digitalization of records. CONCLUSION: Nurses and midwives play a critical role in shaping ANC adherence through service delivery and patient education. Their consistent engagement and ability to build trust with pregnant women make them key influencers in promoting timely and sustained ANC attendance. Addressing systemic challenges, strengthening community-based support, and enhancing policy implementation are essential strategies for improving ANC adherence in Rwanda. These findings provide valuable insights for policymakers and healthcare stakeholders to develop targeted interventions aimed at increasing ANC adherence and improving maternal and neonatal health outcomes.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".