UNDERSTANDING SEVERITY OF ELDER MISTREATMENT: FINDINGS FROM THE CANADIAN LONGITUDINAL STUDY ON AGING
Bibliographic record
Abstract
Abstract Basic elder mistreatment knowledge on risk factors and prevalence has advanced substantially over the past two decades. However, research in this area has predominantly framed our understanding of elder mistreatment in binary (no/yes) terms. While a binary understanding of elder mistreatment is necessary to determine prevalence/incidence, this conventional framing obscures the variation in lived experiences across cases and provides limited clinical insights for response interventions seeking to reduce harm. Building on prior research examining elder mistreatment through a lens of severity, this study analyzed data from the population-based Canadian Longitudinal Study on Aging to both describe the range of elder mistreatment severity across cases and identify longitudinal risk/protective factors associated with severity levels. An ecological-systems perspective organized potential risk/protective factors from several levels of influence. Drawing on a nationally stratified, random sample (n=23,468) of community-dwelling adults aged 65 or older interviewed over a three-year period, the current study analyzed a subsample (n=2368) of older adults identifying as victims of emotional, physical, or financial mistreatment. Severity was operationalized according to past-year frequency and multiplicity of mistreatment behaviors experienced. Ordinal/multinomial logistic regression was used to examine static and change risk/protective factors for elder mistreatment in general and for separate subtypes. Older adults with lower physical, cognitive and mental health, higher levels of child maltreatment, who lived with their perpetrator, and identified as female were more likely to experience severe forms of mistreatment. Findings from this study advance our basic knowledge on elder mistreatment phenomena and inform practice in community-based response programs.
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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 | 0.002 |
| 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".