Psychometric evaluation of the heart failure somatic perception scale in Iranian heart failure patients: a cross-sectional study
Bibliographic record
Abstract
Background: This study aimed to evaluate the psychometric evaluation of heart failure somatic perception scale (HFSPS) in Iranian heart failure patients. Materials and methods: A total of 220 heart failure (HF) patients were enroled in the study. Data gathering was conducted via consecutive sampling from August 2022 to April 2023. Face validity, content validity, construct validity, and internal consistency were used to evaluate the validity and reliability of the Persian version of the HFSPS. Construct validity was done through confirmatory factor analysis and convergent validity. Convergent validity between HFSPS and symptom status questionnaire-heart failure was measured using Pearson’s correlation coefficient. Cronbach’s alpha and Macdonald’s omega coefficient were used to evaluate the reliability of instruments. Results: A total of 220 HF patients participated in this study. Their mean age was 66.46 (SD=11.40). Among the participants, 70% were men. The results of the confirmatory factor analysis evaluation showed the goodness of fit indices of the final HFSPS model after modification was within an acceptable range (χ2=306.18 P<0.001, Minimum Discrepancy Function Divided by Degrees of Freedom=2.47, Comparative of Fit Index=0.91, Tucker-Lewis index=0.90, Adjusted goodness of fit index=0.81, Parsimonious norm fit index=0.70, root mean square error of approximation=0.082). Convergent validity between HFSPS and symptom status questionnaire-heart failure indicated a positive and significant correlation. Cronbach’s alpha coefficient in the HFSPS was 0.868, and McDonald’s omega coefficient in the HFSPS was 0.832. Conclusion: Overall, the Persian version of the HFSPS was determined to be a reliable and valid scale among Iranians with HF.
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".