Quality of Life in Elderlies: A Cross-sectional and Field-based Study in Iran
Bibliographic record
Abstract
Background: Quality of life (QOL) in the elderly has not been investigated as much as their life expectancy. Objectives: The present study aimed to evaluate the elders’ QOL. Methods: In this study, 386 elders were selected using the multistage cluster random sampling method. The Leiden-Padua (LIEPAD) questionnaire, consisting of the core components (CCQOL) and moderators (MQOL) of QOL, was used and analyzed with SPSS software. Results: The interviewees’ mean age was 68.12 ± 6.24 years. The QOL score was 83.67 ± 13.75 (out of 127), consisting of 27 (6.9%) elders with low, 316 (81.8%) elders with moderate, and 43 (11%) elders with high levels of QOL. The CCQOL and MQOL scores were 70.68 ± 9.42 (out of 93), and 20.94 ± 2.30 (out of 34), respectively. According to the multivariate analysis, sleep disorders (B = -0.15), osteoporosis (B = -0.14), female gender (B = -0.13), and not being the source of family income (B = -0.13) were inversely correlated with QOL. In contrast, sleep disorders, facing violence, female gender, migraine, psychological diseases, and not being the source of family income were inversely correlated with CCQOL. Sexual problems, facing violence, no supplementary insurance coverage, inability to walk, and migraine had inverse correlations with MQOL. Conclusions: Seven out of ten elders had a moderate level of QOL, while elderly females and elders with chronic diseases or those who were not the source of family income had lower levels of QOL. Accordingly, the elders’ QOL can be improved by integrating the elders’ care programs in the health centers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".