Patient satisfaction towards health care services provided in Ethiopian health institutions: a systematic review and meta-analysis
Bibliographic record
Abstract
The level of patient satisfaction is a direct or indirect measure of services delivered in healthcare institutions. Different primary studies in Ethiopia showed the proportion of satisfied patients toward health services. Patient satisfaction reflects a wide gap between the current experience and the expected services and pushes clients to go to farther located health care facilities and even to more expensive private health care facilities to find quality healthcare services. Inconsistent findings regarding the proportion of patients that are satisfied with the healthcare services in Ethiopia make generalizations difficult at the national level. We have accessed previous studies through an electronic web-based search strategy using PubMed, Cochrane Library, Google Scholar, Embase, and CINAHL and 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. Out of 188 records screened, 41 studies with 17 176 participants fulfilled the inclusion criteria and were included for proportion estimation. The pooled proportion of satisfied patients was 63.7%. Attending a health center (AOR = 2.68; 95% CI = 1.79, 2.85), being literate (AOR = 0.46; 95% CI = 0.28-0.64), being younger than 34 years old (AOR = 2.07; 95% CI = 1.28, 2.85), and being divorced (AOR = 0.58; 95% CI = 0.38, 0.88) were factors identified as being associated with patient satisfaction. The proportion of patient satisfaction in Ethiopia was high based on over 50% satisfaction scale. The Ministry of Health should give more emphasis to improve hospital health care services to further improve patient satisfaction.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".