Patient satisfaction with mental health services in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Satisfaction surveys in mental health services are crucial indicators of the quality of care given by the health institution and the clinical outcomes of people with mental disorders. The magnitude of patient satisfaction with mental health services was reported with various figures in different countries across the globe and in the regions of Ethiopia. The overall degree of satisfaction with mental health care in Ethiopia remained unknown. Therefore, by evaluating the degree of patient satisfaction and its determining factors with mental health services in Ethiopia, this systematic review and meta-analysis study closed the gap. METHODS: The findings of this review were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Seven databases were searched. The quality of included studies was evaluated by using a modified version of the Newcastle-Ottawa Scale (NOS) for cross-sectional studies, and meta-analysis was done by STATA 17. The I2 test was conducted to evaluate heterogeneity across the studies, and for the data identified as heterogeneous, a random-effects model was used during analysis. Egger’s test was also employed to assess possible publication bias. RESULTS: Among 531 screened studies, 10 studies with a total of 3627 patients were included in the final meta-analysis and revealed that the pooled national level of patient satisfaction with Mental health services was 65.97% [95% CI (58.57, 73.37%)]. Variables such as; living in rural are (OR = 1.92, 95%, CI: 1.40–2.44), lower educational status (OR = 2.04, 95%, CI: 1.34–2.74), living near to service providing institutions [1.93 (95% of CI;1.05,2.81)], regular availability of medications [1.61 (95% CI;1.07,2.15)] were positively and significantly associated with higher levels of patient satisfaction with mental health services in Ethiopia. CONCLUSION: In Ethiopia, one-third of psychiatric patients were dissatisfied with the mental health services provided. This finding emphasizes the importance of addressing geographic, educational, and resource disparities to improve patient satisfaction and overall quality of mental health care across the country.
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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".