Quality of Life of People with Cardiovascular Disease: A Descriptive Study
Bibliographic record
Abstract
In Indonesia, cardiovascular disease (CVD) ranks ninth among 22 types of diseases that result in death. Patients with history of CVD may experience various physical and emotional symptoms such as fatigue, edema, and sleeping difficulties that limit their physical and social activities which will in turn result in poor quality of life. Hospitalization and mortality has been associated with poor quality of life therefore people with history of CVD should be assessed appropriately to determine its impact on patients' daily lives. This study aimed to examine the quality of life in older people with history of heart diseases. This study was conducted in an outpatient department of a private hospital in Tangerang, Banten Province, Indonesia. This quantitative, descriptive study involved 397 older patients. Data were collected via purposive sampling. Older patients with a history of CVD (cardiovascular diseases), aged 60-74 years, who could communicate and understand the Indonesian language and were not in a state that hindered completing a questionnaire were included in the study. Quality of life was measured using the WHOQOL-BREF questionnaire, which comprises four domains: physical health, psychological aspects, social relationships, and environment. Data were analyzed using a descriptive analysis. The results showed that 94% of respondents with a history of CVD had a good quality of life, with 85% having an adequate environmental aspect, 60.7% having active social relationships, 54.7% having good physical health, and 44.8% having a stable psychological condition. Despite having a history of CVD, most respondents in this study reported a good quality of life. However, the measurement tool used in this study measured quality of life in general. Future research should consider using a tool that is specific for measuring the quality of life of people with cardiac diseases.
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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 | 0.000 | 0.000 |
| 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".