The Mediating Effect of Patient Satisfaction in the Patients' Perceptions of Healthcare Quality – Patient Trust Relationship
Bibliographic record
Abstract
The main purpose of this study is to investigate the relationship between patient perception of healthcare quality,patient satisfaction, and patient trust and the mediating effect of patient satisfaction. Study aim also to test thesignificance of socio-demographic variables in determining healthcare quality, patient satisfaction, and patienttrust. Patient perception of healthcare quality was measured using modified SERVQUAL model and resultsindicate that it appears to be a consistent and reliable scale. Finding indicate that, while patient perception ofhealthcare quality has a strong and positive impact on the patient satisfaction and patient trust , patientsatisfaction has also significant impact on patient trust. Moreover, patient satisfaction appears to play animportant mediating role in increasing the strength of the association between healthcare quality and patient trustin healthcare service provider. Results confirm the varying importance of some socio-demographic variables onpatient perception of healthcare quality, patient satisfaction, and patient trust. It has also been found that privatehospitals have higher overall healthcare quality than public hospitals. Study indicate that patient of privatehospitals are more satisfied and feel more trust in healthcare service provider than public hospitals.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".