When Helping Hurts: Nonabusing Family, Friends, and Neighbors in the Lives of Elder Mistreatment Victims
Bibliographic record
Abstract
Purpose of the Study: Elder mistreatment is an epidemic with significant consequences to victims. Little is known, however, about another affected group: nonabusing family members, friends, and neighbors in the lives of the older victim or "concerned persons." This study aimed to identify (a) the prevalence of adults aged 18 and older who have encountered an elder mistreatment situation, (b) the proportion of these who helped the elder victim, and (c) the subjective levels of distress experienced by respondents who helped the victim versus those who did not. Design and Methods: Data were collected from a nationally representative telephone survey of 1,000 adults (18+). Multiple linear regression was used to test the relationship between "helping status" and personal distress attributed to an elder mistreatment, defined as someone aged 60 and older experiencing violence, psychological abuse, financial exploitation, or neglect by a caregiver. Results: Nearly 30% of adults knew a relative, friend, or neighbor who experienced elder mistreatment. Of these, 67% reported personal distress resulting from the mistreatment at a level of 8 or more out of 10. Assuming a helping role was associated with significantly higher levels of personal distress. Greater distress was also associated with being a woman, increasing age, and lower household income. Implications: Knowing about an elder mistreatment situation is highly distressing for millions of adults in the United States, particularly for those assuming a helping role. We suggest intervention approaches and future research to better understand the role and needs of concerned persons.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".