The Impact of Visit Frequency on the Relationship between Service Quality and Outpatient Satisfaction: A South Korean Study
Bibliographic record
Abstract
OBJECTIVE: To examine the relative impact of four service quality dimensions on outpatient satisfaction and to test the invariance of the structural relationships between the service quality dimensions and satisfaction across three patient groups of varying numbers of prior visits to the same hospital as outpatients. DATA SOURCES/STUDY SETTING: Survey of 557 outpatients using a self-administered questionnaire over a 10-day period at a general hospital in Sungnam, South Korea. DATA COLLECTION: Patients answered questions related to two main constructs, patient satisfaction and health care service quality. The health care service quality measures (30 items) were developed based on the results of three focus group interviews and the SERVQUAL scale, while satisfaction (3 items) was measured using a previously validated scale. STUDY DESIGN: Confirmatory factor analysis was used to assess the construct validity of the service quality scale by testing convergent and divergent validity. A structural equation model specifying the four service quality dimensions as exogenous variables and patient satisfaction as an endogenous variable was estimated to assess the relative impact of each of the service quality dimensions on satisfaction. This was followed by a multigroup LISREL analysis that tested the invariance of structural coefficients across three groups with different frequencies of outpatient visits to the hospital. PRINCIPAL FINDINGS: Findings support the causal relationship between service quality and satisfaction in the context of the South Korean health care environment. The four service quality dimensions showed varying patterns of impact on patient satisfaction across the three different outpatient groups. CONCLUSION: The hospital management needs to be aware of the relative importance of each of the service quality dimensions in satisfaction formation of outpatients, which varies across different hospital utilization groups, and use this in strategic considerations.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".