Construct validity of the Heart Failure Screening Tool (Heart‐Fa<scp>ST</scp>) to identify heart failure patients at risk of poor self‐care: Rasch analysis
Bibliographic record
Abstract
AIM: The aim of this study was to psychometrically evaluate the Heart Failure Screening Tool (Heart-FaST) via: (1) examination of internal construct validity; (2) testing of scale function in accordance with design; and (3) recommendation for change/s, if items are not well adjusted, to improve psychometric credential. BACKGROUND: Self-care is vital to the management of heart failure. The Heart-FaST may provide a prospective assessment of risk, regarding the likelihood that patients with heart failure will engage in self-care. DESIGN: Psychometric validation of the Heart-FaST using Rasch analysis. METHOD: The Heart-FaST was administered to 135 patients (median age = 68, IQR = 59-78 years; 105 males) enrolled in a multidisciplinary heart failure management program. The Heart-FaST is a nurse-administered tool for screening patients with HF at risk of poor self-care. A Rasch analysis of responses was conducted which tested data against Rasch model expectations, including whether items serve as unbiased, non-redundant indicators of risk and measure a single construct and that rating scales operate as intended. RESULTS: The results showed that data met Rasch model expectations after rescoring or deleting items due to poor discrimination, disordered thresholds, differential item functioning, or response dependence. There was no evidence of multidimensionality which supports the use of total scores from Heart-FaST as indicators of risk. CONCLUSION: Aggregate scores from this modified screening tool rank heart failure patients according to their "risk of poor self-care" demonstrating that the Heart-FaST items constitute a meaningful scale to identify heart failure patients at risk of poor engagement in heart failure self-care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".