Bibliographic record
Abstract
Background and Objective: Global demographic pattern changes have increased the aging population of countries.A vulnerable population whose health is very important to governments, and in the meantime the quality of life is one of the most challenging health issues in this group.The aim of this study was to investigate the Health related quality of life (HRQOL) in elderly people in Sabzevar.Materials and Methods: This is a descriptive study.The statistical population of this study was 400 elderly people of 60 years and above in Sabzevar, who have been studied by sharing ratio method from six health centers and two nursing homes (parents).Data were collected through two questionnaires of demographic information and HUI3 and through interviews.For analyzing data, we used SPSS V24 software, descriptive statistics, t-test, chi-square and Fisher test.Results: From between 400 elderly participants, 183 people (45.8%) were male and 217 people (54.3%) were female.The mean age of the elderly people was 71.5 ± 9.3.The overall average health score was 0.48 (0.56 in men and 0.42 in women).5.8 percent of the elderly people have a high level of overall health and 67% of them were at a weak level of health; their level of health has been associated significantly with gender, age, education, occupation and chronic diseases (P-value<0.001).Conclusion: The results have shown that older people, women, elderly, unemployed and sole elderly people and those who report more chronic illnesses have been at a lower health status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.932 | 0.928 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".