ADDRESSING ELDER ABUSE IN ETHNO-CULTURAL MINORITY COMMUNITIES IN BC: MAPPING THE KNOWLEDGE GAP
Bibliographic record
Abstract
This project represents a collaborative effort between academic researchers, elder law practitioners, and not-for-profit multicultural service provider administrators and staff. It addresses elder abuse in relationship to two other salient themes, the law and ethnicity, identified by the National Initiative for the Care of the Elderly as priority areas for research, networking, and knowledge transfer in improving the care of older adults. The project has three main objectives: (1) to explore the nature of elder abuse in the two largest ethnocultural minority communities in BC, the Chinese and South Asians; (2) to conduct a mapping exercise to determine what elder abuse tools – both digital and paper – currently exist for these communities; and (3) to present our assessment to a meta-focus group of key service provider representatives to solicit their feedback on our findings, and to create both a visual map of resources and an agenda for developing needed tools. Two key outcomes that have emerged from this leadership collaboration are: (1) a visual map of existing and needed resources that will be used to build awareness to prevent, recognize, and respond to elder abuse in these communities; and (2) the development of one awareness-building tool for each community – a poster – that fills an identified gap in the current resource pool. In the end, the project served to improve support for elder abuse response mechanisms by: (1) facilitating the sharing of resources across participant organizations and their affiliates; and (2) enhancing already-existing informal and formal networks of support across sectors.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".