Sociodemographic profile, functionality, depression, and frailty as determinants for the risk of abuse and violence against older people in the community: An observational study conducted in Brazil
Bibliographic record
Abstract
This study aimed to analyze the relationship between sociodemographic factors, health, functionality, depression, and frailty with the risk of abuse and violence against younger and older adults individuals. A cross-sectional observational study with a quantitative approach was conducted among Brazilian older adults between April and July 2022. Participants aged 60 years and older were recruited from Brazilian Primary Health Care Units. The Hwalek-Sengstock Elder Abuse Screening Test, Lawton and Brody's Instrumental Activities of Daily Living Scale, Edmonton Frail Scale, and the Geriatric Depression Scale (GDS-15) were used to assess the variables of interest. Odds Ratios (ORs) was calculated using binary logistic regression models to test the study hypothesis. The sample was divided into two groups: younger elderly individuals (aged 60-70 years) and older elderly individuals (> 70 years). A total of n = 200 individuals' participants were included in the study (n = 132 younger and n = 68 older). Non-white skin color (n = 15/ 22.1%/ p = 0.016/ OR= 2.0) was identified as a risk factor for the older group, while illiteracy emerged as a risk factor for violence in both groups (OR> 1.0). The absence of depressive symptoms and frailty were protective factors against the risk of abuse and violence in both groups (OR>1.0). Logistic regression analysis indicated that depression was the variable most strongly associated with the risk of abuse and violence, particularly in the younger group (R² = 0.46/ p < 0.001/ ß = 0.56). Among the observed associations, non-white skin color was a risk factor for abuse and violence in the older group, whereas literacy, absence of depression, and absence of frailty were protective factors in both groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".