Prevalence of and Risk Factors for Elder Abuse and Neglect in the Community: A Population‐Based Study
Bibliographic record
Abstract
OBJECTIVES: To estimate past-year prevalence and identify risk and protective factors of elder emotional abuse, physical abuse, and neglect. DESIGN: Cross-sectional, population-based study using random-digit-dial sampling and direct telephone interviews. SETTING: New York State households. PARTICIPANTS: Representative (race, ethnicity, sex) sample (N = 4,156) of English- or Spanish-speaking, community-dwelling, cognitively intact individuals aged 60 and older. MEASUREMENTS: The Conflict Tactics Scale was adapted to assess elder emotional and physical abuse. Elder neglect was evaluated according to failure of a responsible caregiver to meet an older adult's needs using the Duke Older Americans Resources and Services (OARS) scale. Caseness thresholds were based on mistreatment behavior frequencies and elder perceptions of problem seriousness. RESULTS: Past-year prevalence of elder emotional abuse was 1.9%, of physical abuse was 1.8%, and of neglect was 1.8%, with an aggregate prevalence of 4.6%. Emotional and physical abuse were associated with being separated or divorced, living in a lower-income household, functional impairment, and younger age. Neglect was associated with poor health, being separated or divorced, living below the poverty line, and younger age. Neglect was less likely in older adults of Hispanic ethnicity. CONCLUSION: Elder abuse and neglect are common problems, with divergent risk and protective factor profiles. These findings have direct implications for public screening and education and awareness efforts designed to prevent elder mistreatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".