Health Care Workers’ Perspectives of the Influences of Disrespectful Maternity Care in Rural Kenya
Bibliographic record
Abstract
While disrespectful treatment of pregnant women attending health care facilities occurs globally, it is more prevalent in low-resource countries. In Kenya, a large body of research studied disrespectful maternity care (DMC) from the perspective of the service users. This paper examines the perspective of health care workers (HCWs) on factors that influence DMC experienced by pregnant women at health care facilities in rural Kisii and Kilifi counties in Kenya. We conducted 24 in-depth interviews with health care workers (HCWs) in these two sites. Data were analyzed deductively and inductively using NVIVO 12. Findings from HCWs reflective narratives identified four areas connected to the delivery of disrespectful care, including poor infrastructure, understaffing, service users’ sociocultural beliefs, and health care workers’ attitudes toward marginalized women. Investments are needed to address health system influences on DMC, including poor health infrastructure and understaffing. Additionally, it is important to reduce cultural barriers through training on HCWs’ interpersonal communication skills. Further, strategies are needed to affect positive behavior changes among HCWs directed at addressing the stigma and discrimination of pregnant women due to socioeconomic standing. To develop evidence-informed strategies to address DMC, a holistic understanding of the factors associated with pregnant women’s poor experiences of facility-based maternity care is needed. This may best be achieved through an intersectional approach to address DMC by identifying systemic, cultural, and socioeconomic inequities, as well as the structural and policy features that contribute and determine peoples’ behaviors and choices
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".