PILOTING THE ADAPTED KIMBERLY INDIGENOUS COGNITIVE ASSESSMENT TOOL WITH INDIGENOUS SENIORS IN CANADA
Bibliographic record
Abstract
Dementia has become a growing public health issue in an aging Indigenous population in Canada. The Canadian Consortium on Neurodegeneration in Aging (CCNA) includes a research team specifically addressing issues related to quality of life for Indigenous people with dementia and their caregivers. A key component of the work encompasses development of a culturally relevant and psychometrically sound cognitive assessment screening tool. Current cognitive assessments present varying degrees of cultural, educational and language bias, impairing their application in Indigenous communities. This paper reports on the piloting and evaluation of an adapted Indigenous cognitive assessment tool in First Nations communities in Canada. Using community-based participatory methods and a “two-eyed seeing approach,” researchers worked closely with community partners to adapt the Kimberly Indigenous Cognitive Assessment (KICA) for use with Indigenous populations in Northern Ontario, Canada. The KICA was developed to address the gap of culturally appropriate assessment tools for older Indigenous people in Australia. The adaptation involved an iterative process where an advisory group, expert Anishinaabe language speakers, team members, and a key expert panel analysed each assessment domain and adjusted the questions to reflect the local cultural understandings and nuances within the Anishinaabe language. The adaptation of the KICA produced a culturally relevant cognitive assessment tool that was piloted with Indigenous participants from seven First Nations communities in Ontario, Canada. The assessment was provided in English or Anishinaabemowin. Culturally appropriate diagnosis and screening may lead to earlier, more accurate diagnosis and improved health outcomes for Indigenous people with dementia in Ontario.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".