Risk of violence in elderly people in Brazil: representativeness of the age group // Risco de violência em pessoas idosas no Brasil: representatividade da faixa etária
Bibliographic record
Abstract
This study aimed to identify the association and risk factors according to the age group of the elderly. This is an observational, cross-sectional study with a quantitative approach carried out with 200 elderly people assisted by Primary Care in Foz do Iguaçu, PR. They were surveyed using instruments for sociodemographic and health characterization, functionality (Lawton and Brody Scale for Instrumental Activities of Daily Living - IADL), frailty (Edmonton Frail Scale - EFE), depression (Geriatric Depression Scale- GDS-15), and risk of violence (Sclate Hwalek-Sengstock Elder Abuse Screening Test - H-S/EAST). The data were analyzed using Excel software version 2010 and the Statistical Package for the Social Sciences. Chi-square and Odds Ratio with 95% CI were used, with a p-value < 0.05. For the 60-70 age group and the over-70 age group, the risk of violence was associated with schooling, functionality, depression, and frailty. The risk of violence was also associated with race for those aged over 70. All the results were significant for the group with no risk of violence. Violence against the elderly is a public health problem, and health managers need to work together to implement new public policy strategies aimed at promoting and protecting this segment of the population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".