Quality of Life for Saudi Patients With Heart Failure: A Cross-Sectional Correlational Study
Bibliographic record
Abstract
<p><strong>INTRODUCTION:</strong> Heart failure is a major public health issue and a growing concern in developing countries, including Saudi Arabia. Most related research was conducted in Western cultures and may have limited applicability for individuals in Saudi Arabia. Thus, this study assesses the quality of life of Saudi patients with heart failure.</p> <p><strong>MATERIALS &amp; METHODS:</strong><em> </em>A cross-sectional correlational design was used on a convenient sample of 103 patients with heart failure. Data were collected using the Short Form-36 and the Medical Outcomes Study-Social Support Survey.</p> <p><strong>RESULTS:</strong> Overall, the patients’ scores were low for all domains of Quality of Life. The Physical Component Summary and Mental Component Summary mean scores and SDs were (36.7±12.4, 48.8±6.5) respectively, indicating poor Quality of Life. Left ventricular ejection fraction was the strongest predictor of both physical and mental summaries.</p> <p><strong>CONCLUSION:</strong> Identifying factors that impact quality of life for Saudi heart failure patients is important in identifying and meeting their physical and psychosocial needs.</p>
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".