Social and Economic Predictors of Worse Frailty Status Occurrence Across Selected Countries in North and South America and Europe
Bibliographic record
Abstract
BACKGROUND: Frailty, a state of vulnerability to poor resolution of homoeostasis after a health stressor, may be a result of cumulative decline in many physiological systems across the life course and its prevalence and incidence rates vary widely depending on the place and population subgroup. OBJECTIVE: This study aims to examine social and economic factors as predictors of worse frailty status over 2 years of follow-up in a sample of community-dwelling older adults from the International Mobility in Aging Study. METHODS: = 1,724) data on participants from a populational-based, longitudinal study conducted in 4 countries (e.g., Brazil, Colombia, Albania, and Canada). Frailty was defined according to the Fried's phenotype and Poisson regression models with robust standard errors were performed to estimate the relative risks of becoming frail. RESULTS: In our study, 366 (21.2%) participants migrated to a worse stage of frailty. After statistical adjustment (e.g., participant age, sex, and study site), insufficient income (RR = 1.40; 95% CI = 1.00-1.96) and having partner support (RR = 0.80; 95% CI = 0.64-1.01) were predictors of incident frailty status. CONCLUSION: Notably, transitions in frailty status were observed even in a short range of time, with sociodemographic factors predicting incident frailty.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".