Consequences of Elder Abuse and Neglect: A Systematic Review of Observational Studies
Bibliographic record
Abstract
This article presents the results of a systematic review of the consequences of elder abuse and neglect (EAN). A systematic search was conducted in seven electronic databases and three sources of gray literature up to January 8, 2016, supplemented by scanning of citation lists in relevant articles and contact with field experts. All observational studies investigating elder abuse as a risk factor for adverse health outcomes, mortality, and health-care utilization were included. Of 517 articles initially captured, 19 articles met our inclusion criteria and were analyzed. Two reviewers independently performed abstract screening, full-texts appraisal, and quality assessment using the Newcastle-Ottawa Scale. Across 19 studies, methodological heterogeneity was a prominent feature; seven definitions of EAN and nine measurement tools for abuse were employed. Summary of results reveals a wide range of EAN outcomes, from premature mortality to increased health-care consumption and various forms of physical and psychological symptoms. Higher risks of mortality emerged as the most credible outcome, while the majority of morbidity outcomes originated from cross-sectional studies. Our findings suggest that there is an underrepresentation of older adults from non-Western populations and developing countries, and there is a need for more population-based prospective studies in middle- and low-income regions. Evidence gathered from this review is crucial in upgrading current practices, formulating policies, and shaping the future direction of research.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".