Levels and Determinants of Person-Centered Maternity Care Among Women Living in Urban Informal Settlements: Evidence from Client Exit Surveys in Nairobi, Lusaka and Ouagadougou
Bibliographic record
Abstract
Abstract Background Sub-Saharan Africa’s rapid urbanization has led to the sprawling of urban informal settlements. The urban poorest women are more likely to experience worse health outcomes and poor treatment during childbirth. This study measures levels of person-centered maternity care (PCMC) and identifies determinants of PCMC among women living in urban informal settlements in Nairobi, Lusaka and Ouagadougou. Methods We conducted phone, home-based or facility-based exit surveys of women discharged from childbirth care in facilities serving urban informal settlements. We estimated overall and domain-specific PCMC scores covering dignity and respect, communication and autonomy, and supportive care. We ran multilevel linear regression models to identify structural, intermediary and health systems factors associated with PCMC. Results We included 1,249 women discharged from childbirth care: the majority were aged 20-34 years and were unemployed. In Lusaka and Nairobi, over 65% of women had secondary education, and over half gave birth in a hospital, whereas in Ouagadougou a third had secondary education and 30.4% gave birth in a hospital. The mean PCMC score ranged from 57.1% in Lusaka to 73.8% in Ouagadougou. Across cities, women reported high dignity and respect mean scores (73.5% -84.3%), whereas communication and autonomy mean scores were consistently poor (47.6% - 63.2%). In Ouagadougou, women with formal employment, those who delivered in a private for-profit facility, and whose newborn received postnatal care before discharge reported significantly higher PCMC. In Nairobi and Lusaka, women who were attended by a physician during childbirth, and those whose newborn was checked before discharge reported significantly higher PCMC. Conclusion Women living in urban informal settlements experience inadequate PCMC and report poor communication with health providers. Select health systems and provision of care factors are associated with PCMC in this context. Quality improvement efforts are needed to enhance PCMC and ensure women’s continuity in care seeking. Key Messages What is already known on this topic Despite high use of maternal and newborn health services in urban areas, health outcomes still remain worse among lower-income populations, and we know little about the quality of services and experience of care among the urban poorest women. Studies suggest that women who experience disrespect and abuse during childbirth are more likely to discontinue using health services. Person-centered maternity care (PCMC) refers to care that is respectful of and responsive to women’s needs, preferences and values. Previous studies have reported sub-optimal levels of person-centered maternity care in low-and middle-income settings. We conducted this study to evaluate the levels of PCMC and identify structural, intermediary and health systems factors associated with PCMC among low-income urban women living in informal settlements in sub-Saharan African capital cities. What this study adds Women living in urban informal settlements in Nairobi, Lusaka and Ouagadougou experience inadequate PCMC, with overall mean scores ranging from 57.1% (51.4 points out of 90) to 73.8% (66.4 points out of 90). Most women reported experiencing dignity and respect during childbirth, but communication with providers was consistently poor, with mean scores ranging from 47.6% (12.8 points out of 27) in Lusaka to 63.2% (17.1 points out of 27) in Nairobi. In Ouagadougou, women with formal employment, those who delivered in a private for-profit facility, and whose newborn received postnatal care prior to discharge reported significantly higher PCMC. In Nairobi and Lusaka, women who were attended by a physician during childbirth, and those whose newborn received postnatal care before discharge reported significantly higher PCMC. How this study affects research, practice or policy Further research is needed to understand health providers’ barriers in offering PCMC and the structures enabling PCMC. Quality improvement efforts aiming to improve interpersonal communication and provider attitudes, such as health provider trainings and mentorship, as well as leadership engagement may be promising avenues to enhance women’s experience of childbirth care in resource-constrained settings such as urban informal settlements in sub-Saharan Africa.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".