Knowledge, Adherence to Lifestyle Recommendations, and Quality of Life Among Koreans With Heart Failure
Bibliographic record
Abstract
Objective: To assess heart failure (HF) knowledge, adherence to lifestyle recommendations, and quality of life (QOL) among Koreans with HF and identify factors influencing QOL. Methods: A cross-sectional and correlational design was used and a total of 142 Koreans with HF were recruited between April 2012 and September 2013. Data were analyzed using multiple logistic regression with SPSS version 21.0. Results: The mean age of participants was 64.1 ± 7.4 years. A higher proportion of participants were male, married, unemployed, had a high education level, and class I New York Heart Association (NYHA) functional status. A higher proportion of participants had ≥2 comorbidities and the most prevalent comorbidity was diabetes. The mean score of HF knowledge was 6.9 (possible range 0-15) and the most frequent incorrect items were “proper actions to reduce thirst” and “causes of leg swelling” in both better and worse QOL groups. Among the recommended lifestyle, pneumococcal vaccination had the least adherence in both groups. Multiple logistic regression showed that patients in NYHA class I, with a higher left ventricular ejection fraction, who had knowledge of “amount of fluid intake a day” and consumed more than moderate alcohol tended to have better QOL. Conclusion: More active interventions targeting HF knowledge in proper actions to reduce thirst, causes of leg swelling, and the amount of fluid intake per day are required. Patients with HF in more serious condition need special attention regarding the risk of worse QOL. The role of alcohol consumption in QOL among HF patients in Korea needs further exploration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".