Elder Abuse Awareness in Faith Communities: Findings from a Canadian Pilot Study
Bibliographic record
Abstract
SUMMARY Faith communities can play a critical role in the prevention of elder abuse and neglect by fostering heightened public awareness of elder mistreatment, as well as providing services to abused elders in the community. Faith leaders are among the most likely groups of care-givers to encounter cases of elder abuse, but unfortunately not all are aware of ways of identifying and effectively dealing with abuse. Religious leaders require training to educate them about elder abuse issues to identify the important roles they can play in prevention, intervention and treatment. Pastoral workers and theology students should also be exposed to educational concepts regarding this problem. In an age of ecumenism and interfaith movements, religious leaders must become a conduit for the well-being and safety of older adults. This paper discusses exploratory work undertaken in Ontario, funded by Health Canada (Ontario Region), the Ontario Trillium Foundation, and Justice Canada to begin to uncover the extent to which faith leaders are aware of instances of elder abuse, and what they might see as their role in addressing such problems in their faith communities. A thorough literature review suggests that while considerable attention has been paid to the issue of elder abuse, researchers have not focused on the role of faith leaders in addressing this complex problem.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".