The effect of functional quality variables and technical quality on patient satisfaction mediated by image
Bibliographic record
Abstract
The purpose of this study is to examine and describe: 1) the influence of functional quality towards the image of the hospital, 2) the influence of technical quality towards the image of the hospital, 3) the influence of image towards the satisfaction of patients of the hospital, 4) the influence of functional quality towards satisfaction of patients at the hospital, 5) the influence of technical quality towards the satisfaction of patients of the hospital, 6) the influ-ence of functional quality to the satisfaction of patients mediated by the image of hospital, 7) the effect of technical quality to the satisfaction of patients mediated by the image of hospital. The approach of the study employed is survey method by collecting data through question-naires. The population was 495 patients of Bahteramas hospital. Sample was taken by random sampling with total sample of 88. Technique of data analysis to answer the problem of re-search hypotheses was partial least square (PLS). Results of the study show that the quality of functional had a positive and significant influence towards the image of the hospital yet in-significant to the satisfaction of the patients. The image of hospital has a positive and signifi-cant effect to the satisfaction of patients. In other side, the study also shows that the role of the hospital as a partial mediation between functional quality and the satisfaction of patients, but the image of hospital does not have any role as a partial mediation between technical quality and the satisfaction of patients.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".