Frailty Syndrome: A Risk Factor Associated With Violence in Older Adults
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to analyze the association between frailty syndrome as a risk factor associated with violence in older adults. METHOD: A cross-sectional study, carried out with older adults, in an emergency care unit of a northeastern Brazilian city was conducted. Three instruments were used: a form for sample characterization (i.e., demographics) and two more scales, namely, the Edmonton Frail Scale and the Hwalek-Sengstock Elder Abuse Screening Test. The results were analyzed through descriptive and inferential statistics, using chi-square or Fisher's exact tests, Spearman's correlation test, and simple logistic regression. RESULTS: The sample included 146 older adults who were over 70 years old (56.6%), male (56.2%), and at risk of violence (69.86%). Among the categorical variables, there was an association between risk and being of a higher age (80.7%, p < 0.001), unemployed (73.7%, p < 0.05), having more than six children (80.8%, p < 0.05), and frail older adults (88.1%, p < 0.001). There was a correlation (p < 0.05) between the numerical variables of the scales of violence and frailty, with a coefficient of 0.40. The simple logistic regression model showed that frailty syndrome increases the risk of violence among older adults. CONCLUSIONS: It was concluded that frailty is a factor that increases the occurrence of risk of violence and provides information to guide nursing action in the field of forensic sciences.
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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".