Public’s Perception and Satisfaction on the Health Care System in Sultanate of Oman: A Cross-Sectional Study
Bibliographic record
Abstract
Abstract Objective There are no “gold standard” parameters to measure patient satisfaction regarding the health care system provided by the government. Most of the developed countries have well-structured health care systems, and they depend on patient satisfaction to evaluate and optimize performance and activities of such systems. The study was conducted to evaluate the Omani population’s satisfaction toward public and private health care systems existing in the country. Materials and Methods A cross-sectional study was conducted with a predesigned and pretested questionnaire that was sent to all regions of the Sultanate of Oman via an electronic link. The questionnaire included 22 questions divided into two sections: (1) public and private health care systems in Oman, and (2) abroad treatments. Results The response rate of the 11 Oman’s governorates was 73.9%. There was an association between gender, age, marital status, and the level of education with the preference for local private hospital’s treatment (p < 0.001). Both males (88.1%) and females (83.9%) preferred to be treated by Omani doctors. The association between gender and the preference to be treated by the Omani doctors was statistically significant (p = 0.016). There was a significant relationship between the overall patient satisfaction regarding the treatment that they received and all of the following parameters: well-trained nurses, competency of doctors, professional behavior, and skill level of the staff. On the other hand, 88% of the participants were unhappy about appointment waiting times to be seen in the tertiary-care hospital. Conclusion The study showed that most of the participants have preferred to be treated by Omani physicians and nurses, however, hospitals need to make operational and working changes in order to decrease the appointment waiting time, as this was found to be one of the most common reasons for population dissatisfaction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".