Elder Abuse Severity: A Critical but Understudied Dimension of Victimization for Clinicians and Researchers
Bibliographic record
Abstract
Purpose of the Study: To describe the variation in severity of elder emotional abuse, physical abuse, and neglect and identify factors associated with more severe forms of elder mistreatment (EM). Design and Methods: Population-based study using random digit-dial sampling and telephone interviews with a representative sample (n = 4,156) of community-dwelling, cognitively intact older adults in New York State. The Conflict Tactics Scale and DUKE Older Americans Resources and Services scales were adapted to assess EM subtypes. For each EM subtype, severity was operationalized by summing the number of different mistreatment behaviors and the frequency of each behavior. Among older adults reporting some degree of mistreatment, ordinal or multinomial regression predicted severity of elder emotional abuse, physical abuse, and neglect. Results: Distribution of EM severity was characterized by a negative/right skew. More severe emotional abuse was predicted by younger age, living with the perpetrator only, Hispanic background, and higher education. Increasing physical abuse severity was associated with younger age and living only with the perpetrator. Higher neglect severity was associated with functional impairment, younger age, living only with the perpetrator, lower income, and lower education. The presence of nonperpetrator others living in the home served a protective function against escalating mistreatment severity. Implications: Extends existing EM risk factor research by operationalizing mistreatment phenomena along a continuum of severity. Findings enhance capacity to screen and report particularly vulnerable EM victims and inform targeted interventions to ameliorate the problem. Incorporation of severity into EM research/measurement reflects the clinical and phenomenological reality of the problem.
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".