Development of an indigenous functional assessment (IFA) tool
Bibliographic record
Abstract
Abstract Background Functional decline is an essential criterion for the clinical diagnosis of dementia. Functional decline is assessed by activities of daily living, which consist of Basic Activities of Daily Living (BADL) and Instrumental Activities of Daily Living (IADL). IADL (e.g., cooking, taking medications, handling finances) is more complex as it requires multiple cognitive processes whereas BADL (e.g., bathing, dressing and toileting) can be performed with the support of habits and routines. IADL are more likely to be vulnerable to the early effects of cognitive decline, thus being the first indication of cognitive deterioration. Various functional assessment tools are available to assess instrumental activities of daily living; however, there is no culturally appropriate functional assessment tool for the Indigenous population. As activities of daily living differ across cultures, the tool developed to assess activities of daily living in one culture cannot assess it in another culture. Also, the existing IADLs are influenced by gender and socio‐cultural factors. Thus, we aim to develop a culturally‐grounded functional assessment tool to be used in Indigenous communities. Method The study will use a community‐based participatory approach to develop a functional assessment tool. An Indigenous community advisory group will be engaged in the research process. A focus group will be conducted with the Indigenous and non‐Indigenous health service providers working with Indigenous communities and in‐depth interviews with the care partner. The qualitative data on the perspectives, personal and professional experiences of health service providers, and the care partner's lived experiences will be analyzed using community‐engaged thematic analysis with the community advisory group's volunteer. The developed tool will undergo several iterative processes from experts and community advisory groups to reach a consensus on the final tool. Result We are establishing relationships with the Indigenous communities as a partner which is an initial step of conducting research in an Indigenous way. The community advisory group formed through this relationship will be engaged meaningfully in every research process step. Conclusion This tool will be the first to be designed with and for the Indigenous care partners in Canada, which will promote a culturally safe assessment environment for diagnosis of dementia.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".