The Impact of Sociodemographic and Psychological Variables on Quality of Life in Patients With Renal Disease: Findings of a Cross-Sectional Study in Greece
Bibliographic record
Abstract
Background: Renal failure is a chronic disease that can have serious effects on patients’ quality of life (QoL). The objective of this study was to investigate the relationship of QoL to sociodemographic variables (gender, age, education, marital status) as well as clinical variables (self-reported mental health, depression and anxiety ) in end-stage renal disease patients (ESRD). For this purpose, measures assessing QoL as well as mental health were used. Method: A total of 144 in-centre haemodialysis (HD) and continuous ambulatory peritoneal dialysis (CAPD/PD) patients were administered the WHOQOL-BREF, GHQ-28, CES-D and STAI questionnaires. Results: Age was found to have an effect on QoL’s physical and social domains, while education on the environment domain. Marital status was observed to have a relationship with the psychological and social domains. Conclusions: Being female, older, less educated and divorced/widowed may relate to a more compromised QoL in ESRD requiring individualized interventions. doi: http://dx.doi.org/10.4021/wjnu44w
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".