Burden among informal caregivers of individuals with heart failure: A mixed methods study
Bibliographic record
Abstract
AIMS: To develop a comprehensive understanding of caregiver burden and its predictors from a dyadic perspective. METHOD: A convergent mixed methods design was used. This study was conducted in three European countries, Italy, Spain, and the Netherlands. A sample of 229 HF patients and caregivers was enrolled between February 2017 and December 2018 from the internal medicine ward, outpatient clinic, and private cardiologist medical office. In total, 184 dyads completed validated scales to measure burden, and 50 caregivers participated in semi-structured interviews to better understand the caregiver experience. The Care Dependency Scale, Montreal Cognitive Assessment, and SF-8 Health Survey were used for data collection. Multiple regression analysis was conducted to identify the predictors and qualitative content analysis was performed on qualitative data. The results were merged using joint displays. RESULTS: Caregiver burden was predicted by the patient's worse cognitive impairment, lower physical quality of life, and a higher care dependency perceived by the caregivers. The qualitative and mixed analysis demonstrated that caregiver burden has a physical, emotional, and social nature. CONCLUSIONS: Caregiver burden can affect the capability of informal caregivers to support and care for their relatives with heart failure. Developing and evaluating individual and community-based strategies to address caregiver burden and enhance their quality of life are warranted.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".