On the Agreement between Patients’ Perceptions and Expectations about the Quality of Hospital Services
Bibliographic record
Abstract
Minimizing the gap and ensuring agreement between patients’ perceptions and expectations is an indication of a better quality of hospital services. This study aimed to examine the agreement between patients’ perceptions and expectations of the quality of hospital services. A cross-sectional design was adopted, and quantitative methods were employed for data collection. The SERVAQUAL tool was used. The sample size was 415 participants. This study was conducted in Jordanian teaching hospitals. The study population was patients who used outpatient clinics in these hospitals. The study found that there is very low agreement between patients’ expectation and their perceptions. Overall, the perceived service quality was significantly lower than the expected service quality across all of the dimensions used to measure the service quality gap (reliability, responsiveness, assurance, empathy, and tangibles). The results suggest regional variation, where patients who sought care at hospitals in Amman have a four-fold higher perception of the quality of services than patients who visited Irbid hospitals. Also, patients who are more highly educated (Diploma, Bachelor, or Higher Studies) have a higher perception than patients who have less than secondary education. Age and gender were found to have no significant association with patients’ perceptions. The findings of this study suggest that there is a gap between patients’ perceptions and expectations. Thus, there is a need to close this gap by improving patient satisfaction with the quality of services.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".