Frailty and sociodemographic and health factors, and social support network in the brazilian elderly: A longitudinal study
Bibliographic record
Abstract
ABSTRACT Objective: To identify and analyze the sociodemographic and health factors and the social support network of the elderly associated with frailty in the assessments carried out between 2007/2008 and 2018. Methods: This is a longitudinal study with elderly people aged ≥65 years living in the community. The instruments used were those for Demographic Profile, the Mini Mental State Examination, the Functional Independence Measure, Lawton and Brody Scale, Geriatric Depression Scale, Minimum Relationship Map for the Elderly, and Edmonton Frail Scale. Descriptive analysis and linear regression were used, all tests with p < 0.05. Results: Of the 189 elderly in the study period (2007/2008–2018), most were 80 years old and over, with an average of 82.31 years old; they were women, with no partner, who lived with other family members and were retired. In the final analysis, regardless of age and sex, a decrease in functional independence, an increase in depressive symptoms, an increase in the number of self-reported illnesses, and an increase in the frailty score were observed. Conclusion: The factors that were associated with the increase in frailty of the elderly during the study period were age, female sex, and no partner. The health team, which includes nurses, shall be aware of changes and develop care plans to prevent or avoid their progression.
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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 | 0.001 | 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) | 0.001 | 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".