Measuring Patient Satisfaction with Vascular Access: Vascular Access Questionnaire Development and Reliability Testing
Bibliographic record
Abstract
PURPOSE: The primary objective of this study is to measure hemodialysis patients' satisfaction with their vascular access. The secondary objective is to evaluate the measurement tool's psychometric properties to assess patient satisfaction with their vascular access. METHODS: We generated a comprehensive list of survey items related to patients' views and satisfaction with their vascular access and administered it to participating in-center hemodialysis patients over 4 months. Following a factor analysis, the items were reduced and rescaled to generate the final short-form vascular access questionnaire (SF-VAQ). The SF-VAQ was then administered to a new cohort of hemodialysis patients on two occasions 1 week apart from February 1, 2012, to April 1, 2014. A repeated measures analysis of variance and psychometric evaluation was conducted. RESULTS: The final SF-VAQ with 13 items and four domains was administered to 132 patients (35 fistulas, 14 grafts and 83 catheters). The mean Likert value for overall satisfaction on a scale of 1 (low) to 7 (high) was 5.98, with catheters, fistulas and grafts with values of 5.92, 6.46 and 5.21, respectively. The test-retest reliability for two occasions is 0.92 and the internal consistency for the first administration is high at 0.84. CONCLUSIONS: The SF-VAQ is a short, simple to administer vascular access-specific questionnaire with robust psychometric properties that can be used to obtain the patient's views on their vascular access. Based on the newly developed SF-VAQ, patients were the most satisfied with fistulas, scores.
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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 | 0.007 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".