Views of nurses and other healthcare workers on interventions to reduce disrespectful maternity care in rural health facilities in Kilifi and Kisii counties, Kenya: analysis of a qualitative interview study
Bibliographic record
Abstract
Objective There is an abundance of evidence illuminating the factors that contribute to disrespectful maternity care in sub-Saharan Africa. However, there is limited documented evidence on how some of the key influences on the mistreatment of women could be addressed. We aimed to document the perspectives of nurses and other healthcare workers on existing and potential strategies embedded at the health facility level to promote respectful delivery of healthcare for women during delivery and on what interventions are needed to promote respectful and equitable treatment of women receiving maternity care in rural Kenya. Design, setting and participants We analysed relevant data from a qualitative study based on in-depth interviews with 24 healthcare workers conducted between January and March 2020, at health facilities in rural Kilifi and Kisii counties, Kenya. The facilities had participated in a project (AQCESS) to reduce maternal and child mortality and morbidity by improving the availability and the use of essential reproductive maternal and neonatal child health services. The participants were mostly nurses but included five non-nurse healthcare workers. We analysed data using NVivo V.12, guided by a reflective thematic analysis approach. Results Healthcare workers identified four interconnected areas that were associated with improving respectful delivery of care to women and their newborns. These include continuous training on the components of respectful maternity care through mentorships, seminars and organised training; gender-responsive services and workspaces; improved staffing levels; and adequate equipment and supplies for care. Conclusions These findings demonstrate some of the solutions, from the perspectives of healthcare workers, that could be implemented to improve the care that women receive during pregnancy, labour and delivery. The issues raised by healthcare workers are common in sub-Saharan African countries, indicating the need to create awareness at the policy level to highlight the challenges identified, potential solutions, and application or implementation in different contexts.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".