Abstract 10933: Informal Caregivers’ Burden and Its Predictors in Heart Failure: A Mixed Methods Study
Bibliographic record
Abstract
Introduction: Caring for relatives with heart failure is emotionally and physically intensive for informal caregivers leading to caregiver burden. Evidence also suggests that caregiver burden affects the quality of life of both components of the patient-caregiver dyad. Previous research provides a limited understanding of the nature of caregiver burden and its influencing factors from a dyadic perspective. Hypothesis: To develop a comprehensive understanding of caregiver burden, and to identify its predictors with a dyadic perspective Methods: A convergent mixed-method approach was used. In total, 184 patients with heart failure and their informal caregivers completed validated scales to measure burden, care dependency, quality of life, and social support and 50 caregivers participated also to a semi-structured interviews to better understand the caregiver experience. 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 patient worse cognitive impairment, lower physical quality of life, and a higher care dependency perceived by the caregivers. Qualitative and mixed analysis demonstrated that caregiver burden has a physical, emotional, and social nature. In particular time constraints, anticipated concerns about personal future, and generalized fatigue and exhaustion were significant predictors of caregiver burden, thereby affecting their caregiving. Conclusions: Caregiver burden can affect the capability of informal caregivers to support and care for their relatives with heart failure. There is a dire need for development and evaluation of individual and community-based strategies to address caregiver burden so as to enhance the quality of life of both caregivers and their relatives with heart failure.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".