Measuring hospital service quality and its influence on patient satisfaction: An empirical study using structural equation modeling
Bibliographic record
Abstract
This paper presents an empirical investigation to measure different dimensions of hospital service quality (HSQ) by gap analysis and patient satisfaction (PS).It also attempts to measure patients' satisfaction with three dimensions extracted from exploratory factor analysis (EFA) by Principle component analysis method and conformity factor analysis (CFA).In addition, the study analyzes relationship between HSQ and PS in the context of Iranian hospital services, using structural equation modeling (SEM) from patients' perspectives.The maximum gap observed in "responsiveness" and the minimum one in "assurance".In addition, patients had the most satisfaction in "trust" with the mean of 3.83 followed by "General Satisfaction" with the mean of 3.68 and they had the least satisfaction in "Acceptance" with the mean of 3.53.Two measurement models were used for measuring hospital service quality and patient satisfaction and one structural model, which showed the relationship between them.The result of this study showed that there was a positive and significant impact from hospital service quality on patient satisfaction (0.463).In addition, there was a positive and significant relationship between hospital service quality and five dimensions.Furthermore, it was shown that patient satisfaction and three dimensions (General Satisfaction, Trust, and Acceptance) were associated with each other, significantly and positively.At last management strategies and practical suggestions were presented to hospital.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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".