The Caregiver Contribution to Heart Failure Self‐Care (CACHS): Further Psychometric Testing of a Novel Instrument
Bibliographic record
Abstract
Caregivers (CGs) contribute important assistance with heart failure (HF) self-care, including daily maintenance, symptom monitoring, and management. Until CGs' contributions to self-care can be quantified, it is impossible to characterize it, account for its impact on patient outcomes, or perform meaningful cost analyses. The purpose of this study was to conduct psychometric testing and item reduction on the recently developed 34-item Caregiver Contribution to Heart Failure Self-care (CACHS) instrument using classical and item response theory methods. Fifty CGs (mean age 63 years ±12.84; 70% female) recruited from a HF clinic completed the CACHS in 2014 and results evaluated using classical test theory and item response theory. Items would be deleted for low (<.05) or high (>.95) endorsement, low (<.3) or high (>.7) corrected item-total correlations, significant pairwise correlation coefficients, floor or ceiling effects, relatively low latent trait and item information function levels (<1.5 and p > .5), and differential item functioning. After analysis, 14 items were excluded, resulting in a 20-item instrument (self-care maintenance eight items; monitoring seven items; and management five items). Most items demonstrated moderate to high discrimination (median 2.13, minimum .77, maximum 5.05), and appropriate item difficulty (-2.7 to 1.4). Internal consistency reliability was excellent (Cronbach α = .94, average inter-item correlation = .41) with no ceiling effects. The newly developed 20-item version of the CACHS is supported by rigorous instrument development and represents a novel instrument to measure CGs' contribution to HF self-care. © 2016 Wiley Periodicals, Inc.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".