Elder abuse and neglect in Ireland: results from a national prevalence survey
Bibliographic record
Abstract
OBJECTIVE: To measure the 12-month prevalence of elder abuse and neglect in community-dwelling older people in Ireland and examine the risk profile of people who experienced mistreatment and that of the perpetrators. DESIGN: Cross-sectional general population survey. SETTING: Community. PARTICIPANTS: People aged 65 years or older living in the community. METHODS: Information was collected in face-to-face interviews on abuse types, socioeconomic, health, and social support characteristics of the population. Data were examined using descriptive statistics and logistic regression, odds ratios (OR) and 95% confidence intervals (95% CI) are presented. RESULTS: The prevalence of elder abuse and neglect was 2.2% (95% CI: 1.41-2.94) in the previous 12 months. The frequency of mistreatment type was financial 1.3%, psychological 1.2%, physical abuse 0.5%, neglect 0.3%, and sexual abuse 0.05%. In the univariate analysis lower income OR 2.39 (95% CI: 1.01-5.69), impaired physical health OR 3.41 (95% CI: 1.74-6.65), mental health OR 6.33 (95% CI: 3.33-12.0), and poor social support OR 4.91 (95% CI: 2.1-11.5) were associated with a higher risk of mistreatment but only social support and mental health remained independent predictors. Among perpetrators adult children (50%) were most frequently identified. Unemployment (50%) and addiction (20%) were characteristics of this group.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".