Falls among the non-institutionalized elderly in northern Minas Gerais, Brazil: prevalence and associated factors
Bibliographic record
Abstract
Abstract Objective: To identify the prevalence of falls and associated factors in non-institutionalized elderly persons. Methods: A cross-sectional study featuring a population-based sample of non-institutionalized elderly persons in a city in the north of Minas Gerais was performed. Interviews were conducted in households by trained staff using validated instruments. We investigated the associations between falls and demographic, socioeconomic and health-related factors. After bivariate analysis, the variables associated with falls to a level of 20% were analyzed together using logistic regression, assuming at this stage a significance level of 5%. Results: The studied population was predominantly female, married and with a low educational level. The prevalence of falls was 28.4%. The factors that were associated with falls were: female gender (OR=1.67; 95% CI:1.13 to 2.47); negative self-evaluation of health (OR=1.49; 95% CI: 1.02 to 2.20); impaired functional mobility (Timed Up and Go test >20 seconds) (OR=1.66; 95CI: 1.02-2.74); the occurrence of hospitalization in the previous 12 months (OR=1.82; 95% CI: 1.17 to 2.84); and frailty measured by the Edmonton Frail Scale (OR=1.73; 95% CI: 1.14 to 2.64). Conclusions: The prevalence of falls was high for the population studied and was related to the individual health conditions of the elderly.
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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) | 0.001 | 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".