The moderating effect of self-care self-efficacy and self-care behaviors on the relationship between symptom burden and frailty among heart failure inpatients
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Background The trajectory of heart failure is punctuated by episodes of repeated hospitalization due to heart failure exacerbation. Heart failure inpatients bear a heavy symptom burden, which acts as a strong stimulus to decrease patients' physiological reserve and lead to the development of frailty. Frailty has been shown to be a powerful predictor of heart failure inpatients' poor prognosis. Based on the theory of the Transactional Model of Stress, self-care behavior was suggested to be a cognitive-behavioral coping method to mitigate the detrimental effect elicited by symptom stimulus and improve patients' prognoses. Purpose This study was conducted to examine whether self-efficacy, self-care behaviors including self-care maintenance, symptom perception, and self-care management moderate the relationship between symptom burden and frailty. Methods This was a cross-sectional study with 207 participants recruited from June to December 2022. The Chinese versions of Memorial Symptom Assessment Scale-Heart Failure, Self-care self-efficacy scale, Self-care index of heart failure 7.2 and Edmonton frailty scale were adopted to capture corresponding variables. The moderation effects of self-care self-efficacy and three specific self-care behaviors were explored using the PROCESS macro for SPSS. Results Among recruited patients, most patients were male (61.8%), with the mean age of 71.23. As for the moderating effects analyses, self-care efficacy (P<0.001, ΔR2=0.032), self-care maintenance (P<0.001, ΔR2=0.039) and symptom perception (P<0.001, ΔR2=0.031) rather than self-care management (P=0.06) moderated the detrimental effect of symptom burden on frailty. The results of further analysis showed that providers recommended (P<0.001, ΔR2=0.036) rather than self-initiated (P=0.81) self-care management behaviors moderated the relationship between symptom burden and frailty. Conclusions This study identified the moderating effects of self-care self-efficacy, self-care behaviors in mitigating detrimental effect of symptom burden on frailty. Our study suggested one potential pathway to improve patients' prognoses by decreasing the deleterious effects of symptoms and highlighted the necessity of incorporating self-care into heart failure management.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".