Evaluation of Levels and Determinants of Patient Satisfaction with Primary Health Care Services in Saudi Arabia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background The Saudi Ministry of Health makes continual efforts to provide high-quality preventive services through a large network of primary health care (PHC) centers. Patient satisfaction is integral to measuring health outcomes and the quality of these services. Methods We searched the Cochrane, EMBASE, and Google Scholar databases for studies investigating patient satisfaction with PHC services in Saudi Arabia in the past 10 years. The risk of bias and heterogeneity across the included studies were assessed with Newcastle Ottawa scale and I 2 test, respectively. Review Manger version 5.311 was used for data analysis with the random effect model. The quality of evidence of each outcome was measured with the GRADE approach. Results The review included 3302 Saudi residents from six observational studies conducted in different regions of Saudi Arabia. Most studies included in the review had low risk of bias regarding the studied domains. The review indicated moderate overall satisfaction with PHC services (77.00%) among participants. More than 60% of the participants (63.11% and 82.59%) were satisfied with the continuity and communication of PHC services, respectively, whereas, less than half (41.73% and 46.92%) were satisfied with the accessibility of the PHC services and the health education provided at these centers. Moreover, low satisfaction was found among older patients and those with low educational levels. Other sociodemographic factors did not determine patient satisfaction. Conclusion and Recommendations This review indicated a moderate level of overall patient satisfaction with respect to the targeted satisfaction level for Saudi Ministry of Health 2023 PHC services of 85%. Additional efforts and continuing evaluation by health care providers will be crucial to address the weaknesses in PHC services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".