TD‐P‐08: TESTING LOCALLY DEVELOPED LANGUAGE APPS TO REDUCE CAREGIVER STRESS AND AGING IN PLACE AS IT RELATES TO DEMENTIA IN INDIGENOUS POPULATIONS
Bibliographic record
Abstract
In collaboration with our Community Research Advisory Committee (CRAC) our project introduces and tests locally developed language apps for their applicability, user friendliness, and impacts to individuals affected by dementia and their caregivers. It documents development of cultural competencies over an 18 month period. In collaboration with our Community Research Advisory Committee (CRAC) the project utilizes Indigenous Research Methodologies (IRM) to develop an innovative practice to reduce caregiver stress and allow people with dementia in Indigenous communities to “age in place”. This solution can support aging in place by utilizing the First Nations’ communities’ specific protective factors for prevention. We will examine the suitability, effectiveness and use of five locally developed language apps in the File Hills Qu'Appelle Tribal Council (all inclusive: Cree, Saulteaux, Dakota, Lakota, and Nakota). Ten electronic devices (iPads) pre-loaded with the individual's relevant language app will be used as the test device, and provided to ten caregivers of individuals with dementia. We will use observation to measure this as well as documenting regular feedback from the participants. This project allows us to build a framework to integrate First Nation roles, traditions, perspectives and ways of knowing and knowledge into the delivery of dementia care through language. Development of these cultural competencies within the system will support families and individuals affected by dementia, allowing them to improve their quality of life, normalize use of technology and finally to make recommendations for change. This project is unique because it tests a product and a service that is culturally relevant.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".