Association between Frailty and Depression among Elderly in Nursing Home
Bibliographic record
Abstract
Background: Frailty is described by the collective decline of multiple physiological systems and increased vulnerability to multiple stressors. It is also linked to emotional distress and mental illness, especially depression. Both frailty and depression are correlated with many harmful consequences in the elderly, including decreased quality of life, escalated utilization of health services, and elevated morbidity and mortality. Given the prominence of frailty and depression in the elderly, and the deleterious consequences when they coexist, understanding the association between these factors is essential. Aim: This study aims to analyze the association between frailty and depression among the elderly in the nursing home. Material and Methods: This research was cross-sectional, and conducted at 3 nursing homes in South Sulawesi. Frailty and depression were measured. Frailty was assessed by Edmonton Frail Scale (EFS), while depression was evaluated by Geriatric Depression Scale (GDS). The data were analyzed with the Pearson test in SPSS 25. Results: There were 27 participants, consisting of females 19 (70.3%), and males 8 (29.6%) with a mean age was 73.15±8, included in this study. The mean EFS was 5.89±3.15. The mean GDS result was 3.74±3.14. Frailty has positive strong association with depression (r=0.6, p=0.001). Conclusion: There was a strong and substantial association between frailty and depression among the elderly in the nursing home.
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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".