Exploring the relationship between accreditation and patient satisfaction – the case of selected Lebanese hospitals
Bibliographic record
Abstract
BACKGROUND: Patient satisfaction is one of the vital attributes to consider when evaluating the impact of accreditation systems. This study aimed to explore the impact of the national accreditation system in Lebanon on patient satisfaction. METHODS: An explanatory cross-sectional study of six hospitals in Lebanon. Patient satisfaction was measured using the SERVQUAL tool assessing five dimensions of quality (reliability, assurance, tangibility, empathy, and responsiveness). Independent variables included hospital accreditation scores, size, location (rural/urban), and patient demographics. RESULTS: The majority of patients (76.34%) were unsatisfied with the quality of services. There was no statistically significant association between accreditation classification and patient satisfaction. However, the tangibility dimension - reflecting hospital structural aspects such as physical facility and equipment was found to be associated with patient satisfaction. CONCLUSION: This study brings to light the importance of embracing more adequate patient satisfaction measures in the Lebanese hospital accreditation standards. Furthermore, the findings reinforce the importance of weighing the patient perspective in the development and implementation of accreditation systems. As accreditation is not the only driver of patient satisfaction, hospitals are encouraged to adopt complementary means of promoting patient satisfaction.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".