Development and Validation of a Perioperative Satisfaction Questionnaire
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Satisfaction is considered a valuable measure of outcome of healthcare processes. Only a few anesthesia-related validated questionnaires are reported. Because their scope is restricted to specific clinical contexts, their use remains limited. The objective of the current study was to develop and validate a self-reported questionnaire, Evaluation du Vecu de l'Anesthesie Generale (EVAN-G), assessing the satisfaction of the perioperative period surrounding general anesthesia. METHODS: Development of the EVAN-G questionnaire comprised a phase of item generation and a phase of psychometric validation. The patient sample was generated to be proportionally matched to the population of patients undergoing general anesthesia in France. The structure of the questionnaire was identified studying interitem, item-dimension, and interdimension correlations and factor analyses. Data were concurrently gathered to assess external validity. The discriminant validity was determined by comparison of scores across well known patient groups. Reliability was assessed by computation of Cronbach alpha coefficients and by test-retest. RESULTS: Eight hundred seventy-four patients were recruited in eight anesthesia departments. The EVAN-G includes 26 items; six specific scores and one global index score are available. Correlations between EVAN-G scores and other concurrent measures supported convergent validity. The EVAN-G correlated poorly with age, American Society of Anesthesiologists physical status, total anesthesia time, and number of previous anesthesias. Significantly higher satisfaction was reported by patients older than 65 yr, belonging to the laryngeal mask group. Reliability and reproducibility were shown. CONCLUSION: The EVAN-G adds important information oriented toward patients' perceptions. The authors' approach provides a novel, valid, and reliable tool that may be used in anesthesia practice.
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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 | 0.000 | 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) | 0.000 | 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 it