Elder Abuse And Dementia: A Review Of The Research And Health Policy
Bibliographic record
Abstract
Older adults with dementia may be at high risk for abuse, but the topic has not been well studied. We conducted a literature review to examine the relationships between elder abuse and dementia. We found that psychological abuse was the most common form of abuse among older adults, with estimates of its prevalence ranging from 27.9 percent to 62.3 percent. Physical abuse was estimated to affect 3.5-23.1 percent of older adults with dementia. We also found that many older adults experienced multiple forms of abuse simultaneously, and the risk of mortality from abuse and self-neglect may be higher in older adults with greater levels of cognitive impairment. We summarize programs and policies related to the abuse of older adults with dementia, including adult protective services, mandatory elder abuse reporting, and the Long-Term Care Ombudsman Program. We also summarize aspects of the National Alzheimer's Project Act, the Older Americans Act, and the Elder Justice Act. In spite of a recent increase in research and policy developments on elder abuse, challenges such as insufficient funding, limited knowledge about elder abuse, a lack of funding for the implementation of federal and state programs relevant to elder abuse and dementia, and a lack of dementia-specific training for front-line health care staff persist. Stronger programs targeting the well-being of older adults with dementia are needed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".