Development and Validation of the DiAbeTes Education Questionnaire (DATE-Q) to Measure Knowledge Among Diabetes and Prediabetes Patients Attending Cardiac Rehabilitation Programs
Bibliographic record
Abstract
PURPOSE: Knowledge assessment tools are highly useful in clinical practice, as they help health care teams to customize education and clinical care plans based on the needs of patients. The objective of this study was to develop and validate the DiAbeTes Education Questionnaire (DATE-Q) to measure knowledge among diabetes and prediabetes patients attending cardiac rehabilitation (CR). METHODS: Based on patient information needs, other validated tools and diabetes education standards and current practices, experts developed 20 items to comprise the first version of the DATE-Q. To establish content validity, they were reviewed by an expert panel (n = 12) and patients. Refined items were psychometrically tested in 84 diabetes and prediabetes patients attending CR. The internal consistency was assessed via regularized factor analysis and Cronbach α, and criterion validity with regard to patient education and family income. For interpretability analysis, the minimal clinically important difference (MCID) was estimated using distribution- and anchor-based methods. RESULTS: All items were appropriate for administration in this population according to experts and patients. Three factors were extracted and were generally internally consistent and well defined by the items. Criterion validity was supported by significant differences in mean scores by family income (P < .05). Results showed that increases in knowledge can moderately increase mean steps/d and peak oxygen uptake, with an MCID of 2.13. CONCLUSIONS: This study demonstrated preliminary validity of the DATE-Q. Future research is needed to assess other measurement properties to confirm the applicability of this tool in clinical and research settings.
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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 | 0.010 | 0.018 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".