Community-Based Respite Care
Bibliographic record
Abstract
Caregivers are integral to the quality of life for older adults living with dementia who are aging in place. The lack of training and culturally appropriate resources available for caregivers of Indigenous Peoples living with dementia contributes to the accessibility gap for community-based respite care. Although current modules exist for respite care workers, there are limited culturally safe training modules designed for Indigenous caregivers and family members endeavouring to increase Traditional Knowledges around dementia. Developing an Indigenous, community-based caregiver toolkit for dementia creates culturally safe, accessible resources for community respite care. Guided by Indigenous Research Methodologies and community-based research, Morning Star Lodge, in partnership with File Hills Qu’Appelle Tribal Council at the request of a Community Research Advisory Committee, aims to promote Indigenous community-based models of support and to develop a toolkit for caregivers of people living with dementia and their families. The toolkit includes information such as: understanding dementia, strategies for care, links to online resources for caregivers, and resources specific to Indigenous cultures. The goal is to increase caregivers’ access to dementia related supports through the distribution of the toolkit and to build capacity for the provision of community-based respite care by creating an opportunity for education, training, and increasing awareness in Indigenous communities. Creating a caregiver toolkit will also help to relieve caregiver stress by providing education to family members about community-based respite care and services available.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".