Association Between Functionality, Depressive Symptoms, and Fragility in Elderly Adults in Primary Care
Bibliographic record
Abstract
As life expectancy increases, the disease profile of the population also changes, with a higher prevalence of chronic diseases and reduced functional capacity, which increases the risk of social isolation and vulnerability. The aim of this study was to identify the association between functionality, depressive symptoms, and fragility in elderly adults in primary care. This is an exploratory, descriptive study with a quantitative approach and a cross-sectional design, carried out in a municipality in the interior of southwestern Bahia. The instruments used were the Mini-Mental State Examination (MMSE), a sociodemographic questionnaire on health conditions, the Edmonton Fragility Scale (EFS), and the Self-Reported Fragility Scale. The data were analyzed through descriptive analyses with absolute and relative frequencies and the application of the Chi-square test, adopting a value of p ≤ 0.05. Results: A statistically significant difference was found between elderly adults classified as frail and female gender (p = 0.019), marital status without a partner (p = 0.001), dependence in BADL (p = 0.008), dependence in IADL (p-value = 0.000), and the presence of depressive symptoms (p = 0.000). Conclusion: This study found an association between marital fragility related to being without a partner, dependence in IADL (instrumental activities of daily living), and the presence of depressive symptoms.
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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 | 0.002 |
| 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 | 0.000 |
| 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".