Life Course Social and Health Conditions Linked to Frailty in Latin American Older Men and Women
Bibliographic record
Abstract
BACKGROUND: Gender, social conditions, and health throughout the life course affect functional health in later life. This article addresses two specific hypotheses: i) life-course social and health conditions are associated with frailty; and ii) differential exposure and/or vulnerability of women and men to life-course conditions may account for gender differences in frailty. METHODS: Data originated from a cross-national survey of older adults living in five large Latin American cities. Frailty was defined as the presence of three or more of five criteria: unintentional weight loss (10 pounds during the past year), self-reported exhaustion/poor endurance, weakness (grip strength), limitations in lower extremities, and low physical activity; a prefrail state was defined as the presence of one or two of the above criteria. Associations between frailty and social and health indicators were examined using a proportional odds ordinal logistic regression. RESULTS: Prevalence of frailty varied from 0.30 to 0.48 in women and from 0.21 to 0.35 in men. Childhood (hunger, poor health, and poor socioeconomic conditions), adulthood (little education and non-white-collar occupation), and current social conditions (insufficient income) were associated with higher odds of frailty in both men and women. Comorbidity and body mass index were related to frailty, but their effects differed in women and men. Male/female age-adjusted odds of frailty varied from 1.55 (Bridgetown) to 2.77 (Havana). Differential exposure and vulnerability partially explained differences between women and men. CONCLUSION: Theoretical models to explain gender and social differences in frailty should use a life-course perspective.
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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 | 0.001 |
| 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) | 0.002 | 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".