Implementation Process for the Canadian Indigenous Cognitive Assessment
Bibliographic record
Abstract
As Canada’s population ages, the number of individuals living with dementia is expected to increase. Between April 2020 and March 2021, nearly 477,000 people above age 65 were living with diagnosed dementia in Canada. It is thought that there were likely many more undiagnosed cases within the population (Public Health Agency of Canada, 2024), indicating a need to better screen for and diagnose dementia. In North America, some Indigenous populations show a higher prevalence of dementia than non-Indigenous populations (Jacklin, Walker, and Shawande, 2013; Mayeda et al., 2016). Specifically, dementia prevalence in First Nations people in Alberta is higher than in non-First Nations populations and increasing more rapidly (Jacklin, Walker, and Shawande, 2013). Current widely used cognitive assessments do not account for culture, colonization, or health and social inequalities (Jacklin et al., 2020). This highlights the need for a culturally appropriate cognitive assessment, hence the creation of the Canadian Indigenous Cognitive Assessment (CICA), modeled after the Kimberley Indigenous Cognitive Assessment (KICA) from Australia (LoGiudice et al., 2006; Jacklin et al., 2020; Walker et al., 2021; Marsh et al., 2023). The goal of this paper is to highlight key considerations realized during the implementation process of the CICA and what future work is required for its successful use in communities. Work was done through Dr. Walker’s research team that is partnered with the Anishinabek Nation and Za-Geh-Do-Win Information Clearinghouse. Results were informed by the Indigenous Dementia Research Conference on January 29 - February 1, 2024, where many First Nations community members came together to discuss the impacts of dementia and CICA implementation. The results of this research can provide guidance on the opportunities and challenges when implementing a culturally appropriate tool in First Nations communities in Ontario. This paper also highlights what future steps and research might be needed to better support Indigenous people living with dementia and their communities.
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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 | 0.100 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".