Adverse childhood experiences and elder abuse victimization nexus: A systematic review and meta-analysis
Bibliographic record
Abstract
Adverse childhood experiences (ACEs) are important life course events that can influence elder abuse victimisation (EAV) among older adults. This systematic review and meta-analysis aimed to provide synthesised and consolidated evidence on the existing associations between ACEs and EAV. A systematic search was conducted across six databases, including PubMed, PsycINFO, CINAHL Complete, Scopus, Google Scholar, and the Web of Science. All studies that addressed associations between ACEs, in singular or multiple form, and EAV were included in the review. Meta-analysis of the extracted odds ratios (ORs) and confidence intervals (CIs) was conducted using the common-effect inverse-variance model. Nine studies (cross-sectional design = 7; cohort design = 2) met the inclusion criteria. Included studies examined multiple ACEs and multiple EAVs associations (N = 3); at least single ACE and multiple EAVs (N = 3); any single form of ACE and multiple EAVs (N = 3); multiple ACEs-any single form of EAV nexus (N = 2); multiple ACEs-financial elder abuse association (N = 2); and multiple ACEs-physical elder abuse nexus (N = 2). Pooled ORs and CIs showed statistically significant results for all ACEs and EAVs associations whether in singular or multiple form. The results indicate that interventions designed to reduce ACEs, in singular or multiple form, early in life targeting residential and community-dwelling older adults may be relevant in reducing the incidence of EAV. The life course perspective s be integrated into the planning for support services for children, families, and older adults to prevent EAV in singular or multiple forms in later life.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".