Quality of Labour and Delivery Care Process and Associated Factors in Government Hospitals of Ethiopia: a multilevel analysis
Bibliographic record
Abstract
Abstract Background Ethiopia has one of the highest maternal mortality ratios in Africa. Few have examined the quality of labour and delivery (L&D) care in the country. This study evaluated the quality of routine L&D care and identified patient-and hospital-level factors associated with the quality of care in a subset of government hospitals. Materials and methods This was a facility-based, cross-sectional study using direct non-participant observation carried out in 2016. All mothers who received routine L&D care services at government hospitals (n=20) in one of the populous regions of Ethiopia, Southern Nations Nationalities and People’s Region (SNNPR), were included. Mixed effects multilevel linear regression modeling was employed in two stages using hospital as a random effect, with quality of L&D care as the outcome and selected patient and hospital characteristics as independent variables. Patient characteristics included woman’s age, number of previous births, number of skilled attendants involved in care process, and presence of any danger sign in current pregnancy. Hospital characteristics included teaching hospital status, mean number of attended births in the previous year, number of fulltime skilled attendants in the L&D ward, whether the hospital had offered refresher training on L&D care in the previous 12 months, and the extent of resources available (measured on a 0-100% scale) to provide quality L&D care as defined by the Ethiopian Ministry of Health in 2014. The outcome was measured with a quality of L&D care score (scale 0 to 100) based on adherence to L&D care standards, which had been introduced by the Ethiopian Ministry of Health in 2014. Results On average, the hospitals met two-thirds of the standards for L&D care quality, with substantial variation between hospitals (standard deviation 10.9 percentage points). While the highest performing hospital met 91.3% of standards, the lowest performing hospital met only 35.8% of the standards. Hospitals had the highest adherence to standards in the domain of immediate and essential newborn care practices (86.8%), followed by the domain of care during the second and third stages of labour (77.9%). Hospitals scored substantially lower in the domains of active management of third stage of labour (AMTSL) (42.2%), interpersonal communication (47.2%), and initial assessment of the woman in labour (59.6%). We found the quality of L&D care score was significantly higher for women who had a history of any danger sign (β = 5.66; p-value = 0.001) and for women who were cared for at a teaching hospital (β = 12.10; p-value = 0.005). Additionally, hospitals with lower volume and more resources available for L&D care (P-values < 0.01) had higher L&D quality scores. Conclusions Overall, the quality of L&D care provided to labouring mothers at government hospitals in SNNPR was limited. Lack of adherence to standards in the areas of the critical tasks of initial assessment, AMTSL, interpersonal communication during L&D, and respect for women’s preferences are especially concerning. Without greater attention to the quality of L&D care, regardless of how accessible hospital L&D care becomes, maternal and neonatal mortality rates are unlikely to decrease substantially.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".