Trend of Media Based Discharge Planning Implementation on the Quality of Life of Heart Failure Patients A Bibliometric Analysis
Bibliographic record
Abstract
Heart failure is one of the diseases with the highest mortality in the world, accounting for 17.8 million deaths annually, equivalent to one in three global deaths. One of the leading causes of the increase in heart failure cases is the lack of public awareness regarding the importance of prevention, such as healthy eating patterns, sufficient physical activity, and early detection of heart failure. This study analyses bibliometrics regarding the effectiveness of implementing media-based discharge planning. This study supports nursing roles in patient education, self-care, and reducing readmissions through media-based discharge planning. This study uses the Scopus database as a publication source. The Scopus database has become the primary study source for evaluating research related to the implementation of discharge planning using media from 2014 to 2023. Bibliometric analysis was carried out using VOSviewer 1.6.20 software to map scientific research publications on the care of heart failure patients. The results of this analysis identified 46 articles related to discharge planning in heart failure patients where the global trend shows that there is an increase but is still not very stable in the period 2014 to 2023, the country with the highest number of publications is the United States with 21 publications, followed by the United Kingdom 6 publications, Canada 5 publications, This study found that implementing media-based discharge planning can increase patient understanding in managing their condition and reduce the risk of re-hospitalisation. Effective discharge planning is crucial to improving the quality of life of heart failure patients, and it is important to utilise educational media in the patient discharge process to optimise treatment and prevent disease recurrence.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.042 | 0.073 |
| 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; both teacher heads agree on what is shown here.
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".