Building Patient Trust in the Era of National Health Insurance: Consequences of Healthcare Service Quality, Satisfaction and Health Conditions
Bibliographic record
Abstract
This study aimed to develop a patient trust model that contributes to patient satisfaction and quality healthcare services by focusing on the role of patient health condition as a moderator. Data were collected from three regional general hospitals in East Java, Indonesia, using a questionnaire administered to patients or the families of patients. The proposed model consists of seven constructs. Four represent the quality of healthcare: quality of interaction (five variables), physical environment quality (four variables), outcome quality (three variables), and justice quality (six variables). One construct represents the patient’s health condition (two variables), another represents patient satisfaction (six variables), and the last one is patient trust (six variables). The model was tested using structural equation modeling based on WarpPLS. The goodness-of-fit statistic supported the patient trust model. The hypothesis testing results indicated that physical environment quality, outcome quality, justice quality, and health conditions could predict patient satisfaction. The health condition construct was found to moderate the effect of justice quality on patient satisfaction. Moreover, interaction quality, outcome quality, health condition, and patient satisfaction had an influence on patient trust.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".