The prevalence of elder abuse in institutional settings: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: A recent study has shown that close to one in six older adults have experienced elder abuse in a community setting in the past year. It is thought that abuse in institutions is just as prevalent. Few systematic evidence of the scale of the problem exists in elder care facilities. The aim of this review is to conduct a systematic review and meta-analysis of the problem in institutional settings and to provide estimates of the prevalence of elder abuse in the past 12 months. Methods: Fourteen academic databases and other online platforms were systematically searched for studies on elder abuse. Additionally, 26 experts in the field were consulted to identify further studies. All studies were screened for inclusion criteria by two independent reviewers. Data were extracted, and meta-analysis was conducted. Self-reported data from older residents and staff were considered separately. Results: Nine studies met the inclusion criteria from an initial of 55 studies identified for review. Overall abuse estimates, based on staff reports, suggest that 64.2% of staff admitted to elder abuse in the past year. There were insufficient studies to calculate an overall prevalence estimate based on self-reported data from older residents. Prevalence estimates for abuse subtypes reported by older residents were highest for psychological abuse (33.4%), followed by physical (14.1%), financial (13.8%), neglect (11.6%), and sexual abuse (1.9%). Conclusions: The prevalence of elder abuse in institutions is high. Global action to improve surveillance and monitoring of institutional elder abuse is vital to inform policy action to prevent elder abuse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".