Association between domestic abuse and violence against older adults and frailty, depression, and nutritional status in an urban Brazilian setting
Bibliographic record
Abstract
Objective: To investigate the association between the risk of violence against older adults and factors such as frailty, depressive symptoms, and nutritional status in a population receiving care in Primary Health Care (PHC). Methods: This was an observational, cross-sectional, and quantitative study conducted between June 2023 and March 2024 in the municipalities of Santa Cruz and Macaíba, Rio Grande do Norte, Brazil. The study included 323 older adults (≥ 60 years old) registered in the PHC system. Data collection utilized validated instruments, including the Hurt, Insult, Threaten, Scream - Elder Abuse Screening Test (H-S/EAST) to assess violence risk, the Conflict Tactics Scales Form R (CTS-1) for classifying violence situations, the Edmonton Frailty Scale (EFS) for frailty, the Geriatric Depression Scale (GDS-15) for depressive symptoms, and the Mini Nutritional Assessment (MNA) for nutritional status. Statistical analyses included descriptive statistics, chi-square tests, Mann-Whitney U tests, Spearman correlations, and binary logistic regression. Results: Among participants, 46.1% were at risk of violence. Absence of verbal aggression, adequate social support, emotional independence, and better functionality were protective factors against violence. Advanced frailty (OR = 2.43), depressive symptoms (OR = 2.49), and malnutrition (OR = 1.52) were significantly associated with the risk of abuse. Logistic regression identified mood (R² = 0.19) and depressive symptoms (R² = 0.19) as the main predictors of violence. Conclusion: A significant association was found between the risk of abuse and multidimensional factors, particularly a history of violent situations, frailty, and depressive symptoms.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".