Quality of Life for Elderly Residents in Nursing Homes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: More than 8% of Iran's populations are elderly. The greatest challenge in this generation is improvement of health and QoL.The main goal of this study was QoL for elderly residents in nursing homes over 65 years in Golestan Province - Iran. METHODS: This research was an analytical cross study. The population society includes the elderly over 65 years in Golestan Province - Iran. The sample size was calculated based on the correlation of 193 elderly men and women. Therefore, if the correlation is 2.0 or greater is statistically significant at 80% and 0.95 confidence. The needed data collected from two questionnaires Consumer product Safety Commission (CPSC) to assess the QOL of nursing homes and the SF-36 for health QOL the elderly indicators through interviews and observation. The reliability of the CPSC questionnaire was estimated using Cronbach's alpha with a coefficient of 0.838. The SF-36 questionnaire was validated with Cronbach's alpha with a coefficient of 0.95. To analyze data, ANOVA one-way test was used that after investigating homogenization of variances with Levin statistic, if homogenization reported P is rejected, the independent T-test was used to interpret it. RESULTS: Among QOL dimensions only General Health (GH) status showed a significant association with supporting organizations covering status (P = 0.01). The relationship between QOL with marital status in both genders was observed that the General Health (GH) (P = 0.001), Physical Functioning (PF) P = (0.007) Mobility Restricts (MR) P = (0.002), Emotional Problems (EP) (P = 0.001), vitality (V) (P = 0.001), Mental Health (MH) (P = 0.001) were significantly related. CONCLUSIONS: There was a significant relationship between the Physical Functioning (PF) mean and the mean of other QOL indicators in two groups of male and female (P = 0.007), also the safety of nursing homes just related respectively with residence variable (P = 0.01) and their employment (P = 0.031).
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".