A CROSS-CULTURAL COMPARISON OF APPROACHES TO ELDER MISTREATMENT RESEARCH AND INTERVENTIONS
Bibliographic record
Abstract
Elder Mistreatment and Abuse is a growing concern as populations are ageing around the world. Leaders in both research and practice have contributed to a body of literature that provides evidence-based models for assessment and intervention. This symposium brings together world leaders who are the executives of the International Longevity Centers in their countries. Each will discuss the approaches both in research and practice in their respective countries that represent of Argentina, Canada, Japan and the United Kingdom. The president of the International Network for the Prevention of Elder Abuse will open the symposium with an overview of the organization’s mission in preventing elder abuse and raising public awareness. The president of the major foundation the focuses on improving care for older people in the United States, who is also a prominent researcher and practitioner in elder mistreatment, will serve as the discussant to summarize the overall impact of the current research and best practices provided by each of the presenters and to provide a review of how a philanthropic foundation can catalyze programs for elder mistreatment interventions. The panelists will engage in a robust discussion on best practices across cultures and will provide useful information on developing cultural competencies in addressing elder mistreatment in multi-cultural societies. This session will highlight the importance of understanding cultural sensitivity in designing research and developing programs in addressing elder abuse and mistreatment. This is a joint symposium of The John Hartford A. Foundation and the International Longevity Centre Global Alliance.
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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 | 0.096 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".