Exploring women’s views on factors influencing the quality of maternal care among women of reproductive age in Burkina Faso: a fuzzy cognitive mapping study
Bibliographic record
Abstract
BACKGROUND: Despite major investments to improve access to maternal health services in sub-Saharan Africa, quality of care remains a critical barrier to reducing maternal mortality. Women's perspectives are vital for understanding what constitutes quality and identifying priorities for improvement, yet they are rarely integrated into quality measurement frameworks. This study aimed to determine and describe the aspects that women of reproductive age in Burkina Faso consider most important in defining the quality of maternal healthcare. METHODS: This study used a qualitative participatory approach using fuzzy cognitive mapping (FCM) to describe and visualize women's views on quality of maternal care. 32 women of reproductive age identified and weighted factors influencing their experiences, creating visual maps. Participants were recruited through convenience sampling, stratified by age and maternal status. Four FCM sessions were conducted each in two regions, one rural and one urban. Data were analyzed using thematic analysis and centrality measures to categorize influencing factors and determine their relative importance. RESULTS: Women identified inadequate medical equipment, provider availability, hygiene standards, continuity of care, and provider-patient interactions as the strongest determinants of maternal care quality. Emotional support, respectful communication, and the quality of reception at facilities were also emphasized as essential components of positive care experiences. Some indicators commonly used in existing frameworks were rarely mentioned, while women emphasized interpersonal and contextual dimensions often overlooked in standard measures. CONCLUSION: Fuzzy cognitive mapping provided a participatory and visual approach to capturing women's lived experiences and perceptions of quality of maternal care The findings suggest that current measures of quality of care may not fully reflect women's priorities or experiences. Integrating their perspectives into quality measurement frameworks is essential to ensure that maternal care is assessed accurately and that interventions are patient-centered and responsive to women's needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".