INNOVATIVE INTERVENTIONS IN ELDER ABUSE PREVENTION AND MITIGATION
Bibliographic record
Abstract
Since the mid-1970s when elder abuse first came to public attention in the most developed countries, many interventions have been developed by government agencies, NGOs, and other concerned bodies to address abuse and neglect of older adults. In this symposium researchers from Ireland, Israel and Canada will highlight innovative approaches to awareness raising, case identification, mitigation and prevention, training and tool development in their respective countries. In the case of Canada, there are two presentations. One focusses on the province of Quebec where there is no specific public agency to counter elder abuse. A project, funded by the Social Sciences Research Council of Canada, aimed at understanding the actions of NGOs and especially volunteer actions to counter elder abuse is described. The second Canadian presentation and those from Ireland and Israel highlight strengths and weaknesses and/or changes over time of selected interventions implemented in health-care and community-based settings and services.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".