The Impact of Depression and Malnutrition on Health-Related Quality of Life Among the Elderly Iranians
Bibliographic record
Abstract
INTRODUCTION: The present study aimed to assess the association between nutritional status and depressive symptoms among elderly Iranians and to explore their impact on their Health-Related Quality of Life (HRQoL). METHODS: In this cross-sectional study, 447 elders aging from 55 to 85 years were randomly selected and completed the Iranian version of Geriatric Depression Scale-15 (GDS), Mini Nutritional Assessment (MNA), and the Iranian version of Short Form Health Survey (SF-36). RESULTS: Out of the 447 elderly, 72.1% were female with the mean age of 65.99 ± 7.89 years. The prevalence of depression was 38.1%. In addition, the SF-36 sub-scores tended to be lower among the elders with depressive symptoms according to GDS. The Physical Functioning (PF), Bodily Pain (BP), Role Physical (RP), Role Emotional (RE), and Mental Health (MH) dimensions of the SF-36 were also statistically poorer in the elders with depression. The mean MNA score was 24.6 ± 2.7; 35.4% of the participants were malnourished or at risk of malnutrition and 64.6% were adequately nourished. The sub scores of SF-36 were significantly lower in the elders with impaired nutritional status. CONCLUSIONS: Considering the importance of the association among psychological and nutritional problems and HRQoL in caring for and promoting the welfare of the elders, this study provided fundamental information and a basis for further evaluation of this issue in developing and undeveloped countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".