An Empirical Study of the Impact of Service Quality on Patient Satisfaction in Private Hospitals, Iran
Bibliographic record
Abstract
OBJECTIVE: Perceived service quality is the most important predictor of patient satisfaction. The purpose of this study was to investigate the impact of the service quality on the overall satisfaction of patients in private hospitals of Tehran, Iran. METHOD: This cross-sectional study was conducted in the year 2010. The study's sample consisted of 969 patients who were recruited from eight private general hospitals in Tehran, Iran using consecutive sampling. A questionnaire was used for data collection; contacting 21 items (17 items about service quality and 4 items about overall satisfaction) and its validity and reliability were confirmed. Data analysis was performed using t-test, ANOVA and multivariate regression. RESULT: this study found a strong relationship between service quality and patient satisfaction. About 45% of the variance in overall satisfaction was explained by four dimensions of perceived service quality. The cost of services, the quality of the process and the quality of interaction had the greatest effects on the overall satisfaction of patients, but not found a significant effect on the quality of the physical environment on patient satisfaction. CONCLUSIONS: Constructs related to costs, delivery of service and interpersonal aspect of care had the most positive impact on overall satisfaction of patients. Managers and owners of private hospitals should set reasonable prices compared to the quality of service. In terms of process quality, waiting time for visits, admissions, and surgeries must be declined and services provided at the fastest possible time. It should be emphasized to strengthen of interpersonal aspects of care and communication skills of care providers.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".