Seven candidate interventions to address abuse of older people
Bibliographic record
Abstract
The Abuse of Older People - Intervention Accelerator (AOP-IA) project aims to accelerate the development of effective interventions to prevent and reduce AOP aged 60 and older within the framework of the United Nations Decade of Healthy Ageing (2021-2030). The AOP-IA was launched in response to the global need for interventions with proven effectiveness, as few existing approaches have been rigorously evaluated. This paper focuses on the first two phases of the AOP-IA project, which involved conducting a systematic search, screening and evaluation process to identify candidate interventions ready to be rigorously evaluated in future stages of the project, as well as establishing a network of intervention developers. The identification of interventions included an initial screening of 13 926 records and two rounds of evaluations by an expert panel. From this process, 89 promising interventions were identified, and subsequently, seven candidate interventions were selected for more rigorous scientific testing and evaluation. An adapted version of the Systematic Screening and Assessment Method was used to identify these interventions. The AOP-IA project demonstrates that interventions to prevent and reduce abuse of older adults exist in a variety of settings and countries, and that several interventions are ready for a rigorous evaluation to support continual programme improvement by intervention developers and long-term sustainability and scale-up globally.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".