Patient satisfaction with nursing care in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Patient satisfaction with nursing care has been considered as the most important predictor of the overall patient satisfaction with hospital service and quality of health care service at large. However, the national level of patient satisfaction with nursing care remains unknown in Ethiopia. Hence, the objective of this systematic review and meta-analysis was to estimate the level of patient satisfaction with nursing care and its associated factors in Ethiopia. Studies were accessed through an electronic web-based search strategy from PubMed, Cochrane Library, Google Scholar, Embase, PsycINFO, and CINAHL by using a combination of search terms. The quality of each included article was assessed using a modified version of the Newcastle-Ottawa Scale for cross-sectional studies. All statistical analyses were done using STATA version 14 software for windows, and meta-analysis was carried out using a random-effects method. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed for reporting results. Of 1166 records screened, 15 studies with 6091 patients fulfilled the inclusion criteria and were included in the meta-analysis. The estimated pooled level of patient satisfaction with nursing care in Ethiopia was 55.15% (95% CI (47.35, 62.95)). Patients who have one nurse in charge (OR: 1.08, 95% CI: 0.45–2.62, I 2 : 77.7%), with no history of previous hospitalization (OR: 1.37, 95% CI: 0.82–2.31, I 2 : 91.3%), living in the urban area (OR: 1.07, 95% CI: 0.70–1.65, I 2 : 62.2%), and those who have no comorbid disease (OR: 1.08, 95% CI: 0.48–2.39, I 2 : 91.9%) were more likely to be satisfied with nursing care compared with their counterparts although it was not statistically significant. About one in two patients were not satisfied with the nursing care provided in Ethiopia and may be attributed to several factors. Therefore, the Ministry of Health should give more emphasis to the quality of nursing care in order to increase patient satisfaction and improve the overall quality of healthcare service in Ethiopia.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".