Validation of a Scale to Assess Patients’ Comprehension of Frequently Used Cardiology Terminology: The Cardiac TERM Scale in Brazilian Portuguese
Bibliographic record
Abstract
PURPOSE: The aim of this study was to psychometrically validate the translation of a questionnaire on patient understanding of cardiology terminology (TERM) to Brazilian Portuguese. DESIGN: After piloting the translation and cross-cultural adaptation, the 16-item TERM questionnaire was psychometrically tested. METHODS: Internal and test-retest reliability, as well as validity, were assessed in 322 cardiac patients. FINDINGS: Internal (α = .88) and test-retest reliability (all weighted Kappa > 0.63) exceeded the minimum recommended standards. Criterion validity was supported by significant differences in mean scores by socioeconomic indicators (p < .01). Discriminant validity was supported in that cardiac rehabilitation participants had significantly higher TERM scores (p < .001). Participants did not correctly define any of the terms, and a floor effect was identified in all terms. CONCLUSIONS: The Cardiac TERM Scale was demonstrated to have good reliability and validity. CLINICAL RELEVANCE: The scale can be used by healthcare professionals, such as nurses. Results can be used to inform patient education, which could in turn impact patient adherence to medical advice and hence outcomes.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".