Evaluation of the Criteria for Quality of Life of Elderly Health Care Centers in Tehran Province, Iran
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: The objective of this study was to evaluate the elderly quality of life of people covered by the healthcare centers in Tehran and its influencing demographic and background factors. METHOD: This is a cross-sectional study of quality of life of the elderly population covered by healthcare centers and bases in Tehran, as well as the influential background and demographic factors. Sampling was performed using simple random stratified sampling proportionate to the size of strata. Data were collected using the Iranian version of the standard questionnaire Short Form Health Survey (SF-36). RESULTS: According to the findings, 240 (60%) of the cases were men and 160 (40%) were women. Regarding age distribution, 76.3% fell in the 60-69 age range and 87.2% were illiterate. 18% of the elderly stated that they have financial problems and 19.5% did not express any financial problems. While studying the relationship between financial status and health status with the mean scores of quality of life, statistically significant differences were observed in all domains (p=0.032<0.001). The mean quality of life was lower in women compared to men. CONCLUSIONS: The findings of the present study indicate that the health-related quality of life in the elderly population is influenced by their health status and demographic and background variables.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".