Perceived Social Support and Associated Factors among Caregivers of Individuals with Heart Failure: A Convergent Mixed Methods Study
Bibliographic record
Abstract
Background. Caregivers are crucial in ensuring that their relatives with heart failure (HF) reach proper self-care levels. Despite this, the demanding nature of caring for others can lead to poor outcomes and the compromise of own needs, which raises the need for perceived social support. Prior research does not offer a thorough knowledge of how caregivers of people with HF regarded social support and the characteristics that went along with it. Purpose. The aim of this study was to develop a comprehensive understanding of perceived social support and its associated factors among caregivers of individuals with heart failure. Methods. This is a secondary analysis of a convergent mixed-methods design study. The perception of social support, mutuality, anxiety, depression, and quality of life were assessed in 158 heart failure patients and their caregivers (physical and mental dimensions). In 50 caregivers, we also performed semistructured interviews. Results. The mixed analysis and integration of qualitative and quantitative inferences revealed two main factors affecting perceived social support. First, caregivers with strong familial network and greater number of caregivers available for tangible caregiving support and moral support perceived increased sense of social support. Second, caregivers with enhanced mental health had increased sense of social support. Conclusions. Caregiver perception of social support might be influenced by mental well-being status. To improve caregivers’ perceptions of social support and community belonging, it is necessary to create and assess community- and individual-based mental health promotion interventions. To strengthen the perception of support in the heart failure caring process, more dyadic strategies should be established to improve patient-caregiver mutuality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".