Evaluation of a Patient Experience Tool in Dietetic Practice: Validation and Clinical Usage of the Assessment of Registered Dietitian Care Survey (ARCS)
Bibliographic record
Abstract
Purpose: The study aim was to evaluate a patient experience survey, the Assessment of Registered Dietitian Care Survey (ARCS), that is aligned with a nutrition counselling approach (NCA) and evidence-based chronic disease care for use in outpatient registered dietitian (RD) practice. Methods: Criterion and construct validity were examined using Pearson correlation coefficients and principal components analyses, respectively. Reliability was examined using Pearson correlations and Cronbach’s α. Acceptability was evaluated by survey response rate and readability. Kruskall–Wallis test was used to detect differences between RD scores. Results: A total of 479 survey packages were returned (46% response rate). Criterion validity indices were high (r = 0.91 and 0.94, P < 0.001) between Patient Assessment of Chronic Illness Care (PACIC) and NCA subscales, respectively, and lower with overall patient satisfaction (r = 0.63–0.65, P < 0.001). Construct validity revealed 2 factors for PACIC and NCA subscales. There was high internal reliability for the PACIC, 5As, and NCA (Cronbach’s α > 0.7) and test–retest reliability showed an adequate consistency over time (r = 0.70, P < 0.05). The tool was able to detect differences in scores between RDs (P < 0.05). Conclusions: More research is warranted to explore ceiling effects and sensitivity to intervention in similar practice settings. The NCA subscale has acceptable reliability and validity to measure patient experience of RD care.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".