Abstract 12639: Perceived Social Support in Heart Failure Caregiving: A Convergent Mixed-Method Study
Bibliographic record
Abstract
Introduction: Caregivers play an instrumental role in ensuring adequate self-care for their relatives with heart failure (HF). Nevertheless, caregivers can be subject to negative outcomes and sacrifice of personal needs due to their challenging caregiving role, which increases the demand for social support. Previous studies did not provide a comprehensive understanding of perceived social support and its associated factors among caregivers of individuals with HF. Methods: A convergent mixed-methods approach was used. In total, 158 patients with heart failure and their informal caregivers completed validated scales to measure their perceived social support, mutuality, anxiety, depression, and quality of life (physical and mental dimensions). Semi-structured interviews were conducted with 50 caregivers. Multiple linear regression analysis was conducted to identify the predictors of perceived social support, and content analysis was performed on qualitative data. The results were merged using joint displays. Results: Caregivers had a mean age of 57 (14.4 SD) and gave their care to the patients for an average hours per week of 30.38 (28.28 SD). Patients with HF had a mean age of 73.74 (13.46 SD) mostly (48.7 %) in NYHA class II. Quantitative results showed that caregivers perceived better social support when they had a better mental quality of life and higher levels of mutuality. Likewise, when patients’ levels of mutuality were lower, caregivers felt less socially supported. Qualitative findings support that perceived social support was influenced by the caregiver’s mutuality, the patient’s involvement in self-care and the caregiver’ emotional state. Integration of quantitative and qualitative data confirmed that caregivers’ mutuality and emotional state influenced caregivers’ social support. Conclusions: Mental health status can affect caregivers’ ability to perceive social support. There is a need to develop and evaluate individual and community-based strategies to promote caregivers’ mental health for increasing their perceived sense of social support and community belonging. More dyadic strategies should be developed to improve caregiver-caregiver mutuality to increase the sense of support in the heart failure caregiving process.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".