Intervention accelerator to prevent and respond to abuse of older people: insights from key promising interventions
Bibliographic record
Abstract
Globally, abuse of older people (AOP) affects one in six individuals aged 60 years and older every year. Despite the widespread prevalence of AOP, evidence-based interventions for preventing and responding to this issue are insufficient. To address this gap, WHO proposed an initiative to accelerate the development of effective interventions for AOP across all country income levels. In the first phase, the initiative identified 89 promising interventions across a total of 101 evaluations or descriptions, which led to the creation of a public database. Most interventions targeted physical, psychological, and financial abuse and neglect, were implemented in the USA, and focused on victims or potential victims. These interventions were primarily delivered by social workers and nurses, usually in health-care facilities and community centres. Face-to-face delivery was common. Additionally, 28 (28%) of the 101 evaluations used randomised controlled trial designs. The results of this Review can be used to identify interventions that are ready for a rigorous outcome evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".