‘it Is Beyond Our Reach’: Policies and Infrastructure Influencing Postpartum Care in Rural Kenya
Bibliographic record
Abstract
Abstract BackgroundMaternal mortality in low middle-income countries is still high. Like most countries in Sub-Saharan Africa, the progress towards reducing maternal mortalities in Kenya is slow. Approximately 488 women out of every 100,000 live births die during the childbearing process. Kenya has put in place several strategies to mitigate maternal mortalities. For instance, Kenya introduced free maternity services in 2013 to remove financial barriers to skilled health services for mothers and children under five years old. Hence, it is necessary to explore how the policies and infrastructure intersect with other socioeconomic factors to influence postpartum care in rural Kenya to mitigate maternal and infant deaths.MethodsThis qualitative research conducted in-depth focused ethnographic (FE) interviews with 23 nurses and midwives working in nine health centres and the County Hospital in Nandi County, Kenya, between July 2017 and February 2018. We used thematic analysis approach as described by Braun and Clarke to analyze the data. Lincoln and Guba criteria for establishing the trustworthiness of data was used. ResultsThe analysis of data generated six themes. The findings from the theme, Policies and Infrastructure Influencing Postpartum Care will be discussed in this paper. The findings will be discussed under three sub-themes 1) Free maternity services, 2) Adherence to perinatal care guidelines, and 3) Recruitment and retention of nurses and midwives. Facilities lacked the essential equipment and supplies required to provide these services, recruitment and retention of staff, demotivation of healthcare providers, lack of regular training and supervision of staff, and lack of adherence to postpartum guidelines. These issues intersected to determine the quality of skilled postpartum services provided to childbearing women and their families as well as women and infants’ overall perinatal health outcomes. ConclusionThe findings have underscored the importance of having a functional healthcare system that supports both the clinical and emotional aspects of the women and healthcare providers. Efforts should be directed into addressing the negative factors influencing care provision at the facility level. Suboptimal care could cause women not to attend skilled health care and sabotage the global goals of eliminating maternal and infant mortalities. This can be achieved by creating policies that considers the diverse causes and power-relations withing the healthcare organization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".