Informal caregivers of people with heart failure and resilience: A convergent mixed methods study
Bibliographic record
Abstract
AIM: To develop a comprehensive understanding of resilience and its associated factors among informal caregivers of people with heart failure. DESIGN: Transnational multicentre convergent mixed methods approach. METHODS: This study was conducted in three European countries: Italy, Spain and the Netherlands; during February 2017 and December 2018. In total, 195 caregivers completed the Connor-Davidson Resilience Scale, Caregiver Burden Inventory and Hospital Anxiety and Depression Scale. From a nested sample 50 caregivers participated in semi-structured face-to-face interviews. Data were analysed using qualitative content analysis, multiple regression and joint displays. RESULTS: The caregivers' mean age was over 60 years. The quantitative results showed that caregivers experienced anxiety and depression regardless of a good score of resilience and moderate level of burden. Regression analysis showed that the resilience was associated with caregiver depression. From qualitative findings three resilience inhibiting (psychological outlook, physical weariness and affective state) and two promoting factors (community interconnectedness and self-comforting activities) were generated. Mixed analysis confirmed that depression decreased caregivers' resilience. CONCLUSIONS: Caregivers of people with heart failure experience continuous stress and anxiety resulting in reduced resilience. Collaborative efforts are needed to build multifaceted interventions and programs to enhance caregivers' resilience by targeting the factors identified in this study. IMPACT: The quality of informal caregiving is affected by the resilience of caregivers. No research has explored the resilience levels and its factors in this population. Depression, psychological outlook, physical weariness and affective state are negative factors of caregivers' resilience. Personal strategies combined with social and community support and belongingness enhance caregivers' resilience. Community care organizations and hospitals could establish alliances to develop programs for enhancing caregivers' resilience.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.003 |
| Research integrity | 0.001 | 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".