Outpatient Satisfaction at Private General Hospitals in Ho Chi Minh City, Vietnam
Bibliographic record
Abstract
The quality of hospital services remains a concern of both the manager and the patient. The study aims to identify factors affecting outpatient satisfaction at private general hospitals in Ho Chi Minh City, establishing a scale for measuring them. Some 450 outpatients who were treated in five top private hospitals in Ho Chi Minh city (HCMC) in 2019 - An Sinh General Hospital, Hoan My General Hospital, Columbia Asia International Hospital, FV Hospital, and Vu Anh International General Hospital - were interviewed directly in the last quarter of 2019 to obtain the information. The SERVPERF model, plus the cost, together with the SPSS software, have been used to process information by Cronbach's alpha analysis, Exploratory Factor analysis, and linear regression analysis. The results show that there are five factors influencing outpatient satisfaction at private general hospitals in HCMC, in which four factors affects positively in the order of decreasing importance: treatment outcome, doctors and nurses' professional capacity, facilities and environment of the hospital, hospital care, and the treatment time factor affects negatively. The results of the study provide private hospital in HCMC managers with a number of suggestions to increase the level of hospital service quality, so that increase outpatients 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".