Prevalence of and Risk Factors for Elder Abuse and Neglect in the Community: A Population‐Based Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it