A Systematic Review: Risk and Protective Factors of Elder Abuse for Community-Dwelling Racial Minorities
Bibliographic record
Abstract
Elder abuse is related to numerous adverse health and mental health conditions in older adults and some studies indicated higher rates of elder abuse in ethnic and racial minority populations than non-Hispanic Whites. This current study aims to summarize the risk and protective factors associated with elder abuse in community-dwelling racial minorities. A systematic review was conducted following Preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines. AgeLine, Medline, PsycINFO, and CINAHL were searched without limitation on time periods of publication. Two authors independently screened the search results and assessed the eligibility and quality of the retrieved articles. A total of 718 articles were screened and 25 articles were included in the review. The 25 included studies' publication dates range from 1989 to 2019. Five racial categories were generated: African Americans, Asian Americans, Native Americans, Hispanic Americans, and racial minorities in Canada. Risk and protective factors of elder abuse and its subtypes (e.g., physical and psychological abuse) for these racial groups were summarized. There are some common risk factors across racial minorities, such as dependence on caregivers and health issues. The review identified gaps and conflicting findings regarding culture and education that are worthy of further investigation. In particular, there is a lack of current research on elder abuse in racial minority older women, race minority subgroups, and protective factors of elder abuse. The results guide helping professionals to consider the role of traditional culture and identify warning signs of potential abuse among racial minority older adults.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".