Elder abuse prevalence in community settings: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Elder abuse is recognised worldwide as a serious problem, yet quantitative syntheses of prevalence studies are rare. We aimed to quantify and understand prevalence variation at the global and regional levels. METHODS: For this systematic review and meta-analysis, we searched 14 databases, including PubMed, PsycINFO, CINAHL, EMBASE, and MEDLINE, using a comprehensive search strategy to identify elder abuse prevalence studies in the community published from inception to June 26, 2015. Studies reporting estimates of past-year abuse prevalence in adults aged 60 years or older were included in the analyses. Subgroup analysis and meta-regression were used to explore heterogeneity, with study quality assessed with the risk of bias tool. The study protocol has been registered with PROSPERO, number CRD42015029197. FINDINGS: Of the 38 544 studies initially identified, 52 were eligible for inclusion. These studies were geographically diverse (28 countries). The pooled prevalence rate for overall elder abuse was 15·7% (95% CI 12·8-19·3). The pooled prevalence estimate was 11·6% (8·1-16·3) for psychological abuse, 6·8% (5·0-9·2) for financial abuse, 4·2% (2·1-8·1) for neglect, 2·6% (1·6-4·4) for physical abuse, and 0·9% (0·6-1·4) for sexual abuse. Meta-analysis of studies that included overall abuse revealed heterogeneity. Significant associations were found between overall prevalence estimates and sample size, income classification, and method of data collection, but not with gender. INTERPRETATION: Although robust prevalence studies are sparse in low-income and middle-income countries, elder abuse seems to affect one in six older adults worldwide, which is roughly 141 million people. Nonetheless, elder abuse is a neglected global public health priority, especially compared with other types of violence. FUNDING: Social Sciences and Humanities Research Council of Canada and the WHO Department of Ageing and Life Course.
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".