Caregiver Contribution to Heart Failure Self‐Care (<scp>CACHS</scp>)
Bibliographic record
Abstract
AIM: While caregivers (CGs) make an important contribution to the self-care of heart failure (HF) patients, there are no reliable and valid tools for measuring such contributions. Current interventions that strive to optimize patient outcomes through self-care strategies neglect to account for CG contributions, a potential confounder on outcomes. The aim of the study was to develop an instrument that measures CG contributions to HF patients' self-care. DESIGN: The study design follows an established process for instrument development. METHODS: A systematic literature review and semi-structured interviews of CGs were conducted to identify measureable CG activities. Items were derived from thematic analysis of CG narratives. A content validity index was computed for each item (I-CVI). Items with an I-CVI of >0·70 were retained. Items with an I-CVI of 0·50-0·70 were revised for clarification and items with an I-CVI <0·5 were discarded, except in instances where fulsome theoretical or empirical evidence supported their retention. RESULTS: 14 CGs completed interviews and 10 CGs with 4 expert nurses completed I-CVI testing. Major interview themes included arranging appointments, medication adherence, monitoring, coordinating care, encouraging independence and taking action. A total of 36 items were constructed and underwent I-CVI testing. Following I-CVI testing, 27 items were retained, seven items were retained after revision based on CG feedback and two items were removed. This newly developed 34-item questionnaire represents current literature, CGs' experiences, excellent I-CVI scores and ready for further psychometric testing.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".