Development and evaluation of a social inclusion framework for a comprehensive hospital-based elder abuse intervention
Bibliographic record
Abstract
A framework of social inclusion can promote equity and aid in preventing and addressing the abuse of older adults. Our objective was to build a social inclusion framework for a comprehensive hospital-based elder abuse intervention being developed. Potential components of such a framework, namely, health determinants and guiding principles, were extracted from a systematic scoping review of existing responses (e.g., interventions, protocols) to elder abuse and collated. These were subsequently rated for their importance to the elder abuse intervention by a panel of violence experts and further evaluated by a panel of elder abuse experts. The final social inclusion framework comprised 12 health determinants each representing factors underpinning susceptibility for abuse in aging populations: history of trauma/abuse, communication needs, disability, health status, mental capacity, social support, culture, language, sexuality, religion, gender identity, and socioeconomic status. The framework also comprised 19 guiding principles each encompassing considerations for equitable engagement with older adults (e.g., All older adults have the right to self-determination, All older adults have the right to be safe, All older adults are assumed competent unless determined otherwise). Integrating this social inclusion framework into the design and delivery of an elder abuse intervention could empower older adults, while at the same time ensuring that practices and policies are tailored to meet their unique and varying needs.
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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 | 0.243 | 0.137 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".