Incidence and Risk Factors of Elder Mistreatment in the Community: A Longitudinal Population-Based Study
Bibliographic record
Abstract
Abstract Prior population-based elder mistreatment (EM) risk factor research has focused on problem prevalence using cross-sectional designs, which cannot make causal inferences between proposed risk factors and EM or discern existing cases from new cases entering the population. This study sought to estimate the incidence of EM and identify risk factors for new cases. It is a ten-year prospective, population-based cohort study with data collected between 2009 (Wave 1) and 2019 (Wave 2). Based on Wave 1 random, stratified sampling to recruit English/Spanish-speaking, cognitively intact, community-dwelling older adults (age ≥ 60) across New York State, this study conducted computer assisted telephone interviews (CATI) with 628 respondents participating in both Wave 1 and Wave 2 interviews (response rate=60.7%). Ten-year EM incidence was regressed on factors related to physical vulnerability, living arrangement, and socio-cultural characteristics using logistic regression. Ten-year incidence rates included overall EM (11.4%), financial abuse (8.5%), emotional abuse (4.1%), physical abuse (2.3%), and neglect (1.0%). Poor self-rated health at Wave 1 significantly predicted increased risk of new Wave 2 overall EM (odds ratio [OR]=2.8), emotional abuse (OR=3.67), physical abuse (OR=4.21), and financial abuse (OR=2.8). Black older adults were at significantly heightened risk of overall EM (OR=2.61), specifically financial abuse (OR=2.8). Change from co-residence (Wave 1) toward living alone (Wave 2) significantly predicted financial abuse (OR=2.74). Healthcare visits represent important opportunities to detect at-risk older adults. Race is highlighted as an important social determinant for EM requiring urgent attention. This study represents the first longitudinal, population-based EM incidence study.
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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 | 0.001 | 0.000 |
| 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 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".