Deliver on Your Own: Disrespectful Maternity Care in rural Kenya
Bibliographic record
Abstract
BACKGROUND: Under the Free Maternity Policy (FMP), Kenya has witnessed an increase in health facility deliveries rather than home deliveries with Traditional Birth Attendants (TBA) resulting in improved maternal and neonatal outcomes. Despite these gains, maternal and infant mortality and morbidity rates in Kenya remain unacceptably high indicating that more needs to be done. AIM: Using data from the Access to Quality Care through Extending and Strengthening Health Systems (AQCESS) project's qualitative gender assessment, this paper examines women's experience of disrespectful care during pregnancy, labour, and delivery. The goal is to promote an improved understanding of the actual care conditions to inform the development of interventions that can lift the standard of care, increase maternity facility use, and improve health outcomes for both women and newborns. METHODOLOGY: We conducted sixteen focus group discussions (FGDs), two each for adolescent females, adult females, adult males, and community health committee members. As well, twenty-four key Informants interviews (KII) were also conducted including religious leaders, and persons from local government representatives, Ministry of Health (MOH), and local women's organizations. Data were captured through audio recordings and reflective field notes. RESEARCH SITE: Kisii and Kilifi Counties in Kenya. FINDINGS: Findings show nursing and medical care during labour and delivery were at times disrespectful, humiliating, uncompassionate, neglectful, or abusive. In both counties, male health workers were preferred by women giving birth, as they were perceived as more friendly and sensitive. Adolescent females were more likely to report abuse during maternity care while women with disabled children reported being stigmatized. Structural barriers related to transportation and available resources at facilities associated with disrespectful care were identified. CONCLUSIONS: A focus on quality and compassionate care as well as more facility resources will lead to increased, successful, and sustainable use of facility care. Interpreting these results within a systems perspective, Kenya needs to implement, enforce, and monitor quality of care guidelines for pregnancy and delivery including respectful maternity care of pregnant women. To ensure these procedures are enforced, measurable benchmarks for maternity care need to be established, and hospitals need to be regularly monitored to ensure these benchmarks are achieved.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".