Predictors of patient satisfaction and outpatient health services in China: evidence from the WHO SAGE survey
Bibliographic record
Abstract
BACKGROUND: Patient satisfaction is an essential indicator in medical practise and research. To monitor the health and well-being of adult populations and the ageing process, the World Health Organization (WHO) has initiated the Study on Global AGEing and Adult Health (SAGE), compiling longitudinal information in six countries including China as one major data source. OBJECTIVE: The objective of this study was to identify potential predictors for patient satisfaction based on the 2007-10 WHO SAGE China survey. METHODS: Data were analysed using random forests (RFs) and ordinal logistic regression models based on 5774 responses to predict overall patient satisfaction on their most recent outpatient health services visit over the last 12 months. Potential predictor variables included access to care, costs of care, quality of care, socio-demographic and health care characteristics and health service features. Increase of the mean-squared error (incMSE) due to variable removal was used to assess relative importance of the model variables for accurately predicting patient satisfaction. RESULTS: The survey data suggest low frequency of dissatisfaction with outpatient services in China (1.8%). Self-reported treatment outcome of the respective visit of a care facility demonstrated to be the strongest predictor for patient satisfaction (incMSE +15%), followed by patient-rated communication (incMSE +2.0%), and then income, waiting time, residency and patient age. Individual patient satisfaction in the survey population was predicted with 74% accuracy using either logistic regression or RF. CONCLUSIONS: Patients' perceived outcomes of health care visits and patient communication with health care professionals are the most important variables associated with patient satisfaction in outpatient health services settings in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".