Measuring hospital food service quality: Adaptation and validation of the SERVQUAL–HF scale
Bibliographic record
Abstract
Objective: To adapt and psychometrically evaluate the SERVQUAL-HF instrument for hospital food service quality assessment by validating its dimensional structure, reliability, and ability to identify key predictors of patient satisfaction. Methods: This study uses the SERVQUAL framework to assess hospital food service quality, adding variables geared toward meal-specific aspects. A 7-point Likert-scale survey was performed in two hospitals to compare patient expectations to actual reality. Results: Statistical validation, including multilinear regression and correlation analysis, revealed that responsiveness, food quality, perceived value, empathy, and meal variety are all significant predictors of customer satisfaction. SERVQUAL for Hospital Food Service (SERVQUAL-HF)’s dependability in assessing service quality across hospital settings was proven by a psychometric examination. The study emphasizes methodological modifications, such as the significance of empathy and perceived value, and suggests directions for future research in healthcare service measuring. Conclusions: The findings add to the literature by improving the use of SERVQUAL in non-traditional hospital settings, ensuring comprehensive evaluation of patient-centered food service models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".