Validity and reliability of the Korean version of self-care of heart failure index
Bibliographic record
Abstract
Purpose: Self-care and associated decisions for therapeutic recommendations have been a focus of attention recently in Korea. The purpose of this study was to address the dimensionality and reliability of a Korean version of Self-care of Heart Failure Index (SCHFI v.6.2), a measure of self-care of patients with heart failure within a clinical context.Methods: The study sample completed 120 surveys that consisted of demographic variables and the SCHFI v.6.2, which was created to measure self-care maintenance, self-care management, and self-care confidence in HF patients. Confirmatory factor analysis using Mplus verified a robust structural fit of the three dimensionality for each subscale.Results: Self-care maintenance, CFI = .92, TLI = .88, SRMR = .06, RMSEA = .07; self-care management, CFI = .93, TLI = .78, SRMR = .05, RMSEA = .24; self-care confidence, CFI = .95, TLI = .92, SRMR = .05, RMSEA = .13. Multidimensionality yielded the self-care maintenance scale having 4-factor structures, while each self-care management and confidence scale had a unidimensionality. Reliability estimates using methods compatible with each scale’s dimensionality were adequate to high, ranging from .71 to .96.Conclusions: Psychometric testing of the SCHFI demonstrates a sound model fit, with desirable reliability estimates given each scale dimensionality, using Cronbach’s alpha coefficient and alternative options.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".