Needs and problems related to sociodemographic factors of informal caregiving of people with heart failure: A mixed methods study in three European countries
Bibliographic record
Abstract
AIMS: To explore caregivers' needs and problems in three European countries and associate the clusters of caregivers' needs with their sociodemographic characteristics. DESIGN: A qualitative focused mixed methods design was used. METHODS: In total, 52 caregivers of heart failure (HF) people were interviewed in three European countries between March 2017 and December 2018. Transcripts were analysed using the seven-phase method of the exploratory multidimensional analysis according to Fraire with Reinert lexical classes findings were organized in dendrograms. Mayring's content analysis was also performed. RESULTS: Three clusters of caregivers were identified: spouses, adult children and non-family members. Caregivers not only provide HF patients with vital unpaid support for their physical and emotional needs, but they are continually trying to cope with their social isolation and deteriorating health. CONCLUSIONS: Informal caregiving emerged as a complex process influenced by various sociodemographic factors. Gender, relationship type and economic status are the important factors to be considered planning to develop approaches to address the needs of caregivers serving people with heart failure. IMPACT: A comprehensive understanding of the nature of informal caregiving of individuals with heart failure, the complexity of the real-world sociodemographic and cultural factors is warranted. The use of the EMDA method gave us the possibility of processing large masses of qualitative data through rapid, complex calculations. In detail, AATD allowed us to study in deep the significant fuzziness of what caregivers expressed and to analyse the content of the entire interviews and to produce global knowledge by using multi-dimensional statistical methods to grasp the fundamental sense of the interviews, beyond the simple words. Three clusters were identified in the samples, including spouses, adult children and non-family members. This study demonstrated that some sociodemographic characteristics could lead to everyday needs. Therefore, these demographic characteristics should be considered in developing targeted interventions. The research was conducted in Europe, but the technique shown can be replicated everywhere. The findings not only impact nursing but can be extended to all those stakeholders who concur with a public health educational mission. PATIENT OR PUBLIC CONTRIBUTION: Carers were involved in this study after the discharge of their loved ones or at the time of the outpatient visit. They were involved after they had been observed in their dynamics of involvement in caring of the familiars or friends with heart failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".