doi:10.1155/2011/861484 Review Article Evidence-Based Approaches to Remedy and Also to Prevent Abuse
Bibliographic record
Abstract
Copyright © 2011 Donna M. Wilson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Elder abuse is a global issue, with an estimated 4–10 % of older persons in Canada abused each year. Although Canadian legislation has been created to prevent and punish the abuse of older persons living in nursing homes and other care facilities, communitydwelling older persons are at greater risk of abuse. This paper highlights the importance of evidence-based actions targeted at three determinants of health: (a) personal health practices and coping skills, (b) social support networks, and (c) social environments. Two research studies are profiled as case studies that illustrate the ready possibility and value of two specific types of actions on community-based older-person abuse. This paper argues for the immediate and widespread adoption of these evidence-based measures and for additional empirical evidence to guide the correction of underreporting of abuse, raise awareness of its serious nature, and increase options to not only stop it but ultimately prevent it. 1.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.238 | 0.111 |
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".