Validation of the translated version of the EVAN-G scale in a Chinese-speaking population
Bibliographic record
Abstract
BACKGROUND: This study aimed to translate the French version of a perioperative satisfaction questionnaire (EVAN-G) scale, a validated questionnaire for assessing perioperative patient satisfaction, into a Chinese version and validate it in Chinese-speaking patients. METHODS: We developed the Chinese version of the EVAN-G (EVAN-GC) scale based on the original French version of the EVAN-G. The EVAN-GC scale, the Short version of the Spielberger State-Trait Anxiety Inventory (S-STAI), and the McGill pain questionnaire (MGPQ) were administered on the WeChat mini program. We invited patients to complete these questionnaires within 4 to 24 h after surgery. The psychometric validation of the EVAN-GC scale included validity, reliability, and acceptability. RESULTS: Among 220 patients, 217 (98.6%) completed the EVAN-GC scale after surgery. The item-internal consistency revealed good construct validity. Compared with the total scores of the S-STAI and MGPQ, the EVAN-GC scale showed excellent convergent validity (ρ = - 0.32, P < 0.001; ρ = - 0.29, P < 0.001). The EVAN-GC scale could differentiate between groups, which showed good discriminate validity. The Cronbach's alpha coefficient (0.85) of the translated scale demonstrated satisfactory internal consistency reliability, and a 36-patient subsample retest evidenced good test-retest reliability (ρ = 0.82, P < 0.001). In addition, the median [interquartile range] time of completing the EVAN-GC scale was 3.7 [2.9-4.9] min. CONCLUSIONS: The EVAN-GC scale has good psychometric properties similar to those of the original French version. The EVAN-GC scale is a valid and reliable measurement to assess patient satisfaction in Chinese-speaking patients. TRIAL REGISTRATION: The Chinese Clinical Trial Registry, ChiCTR2100049555.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".