P3‐406: Dementia Health Literacy among First Nations People in Ontario: Integrating Western and Indigenous Understandings
Bibliographic record
Abstract
Dementia in Indigenous peoples is expected to be an increasing challenge for health care systems at all levels. Our research team’s previous work resulted in the development of evidence-based culturally grounded health promotion material for First Nations people in Canada. Our current project aims to understand the implications of Indigenous cultural diversity on the effectiveness of the materials. To do this we undertook a process of refinement of the materials from a national level focus to a regional focus with the goal of improving cultural relevance at the local level. Our approach was community-based and participatory. Working with the N’Swaakamok Native Friendship Centre, an Aboriginal advisory group was developed to guide the research. We conducted a qualitative study including 2 focus groups (n=8) with older adults aged 55 and over and 4 one-on-one interview sessions with caregivers of a person with Alzheimer’s disease or age-related dementia. An iterative, thematic data analysis utilizing NVIVO 10 software to organize the data assisted in making meaning. Member checking through transcript reviews and a larger group session was completed to validate the thematic analysis. A significant finding suggests that Aboriginal caregivers and older adults prefer developing a connection with an educated health care professional in order to receive dementia education. This can be done through face-to-face contact by means of family-centred group sessions. These sessions should be supplemented by dementia health promotion material that is organized into the Medicine Wheel. We draw upon the Seven Grandfather Teachings to exemplify how individuals can apply this knowledge in their lives. This research study resulted in two culturally relevant dementia health promotion fact sheets that are locally-specific for the Aboriginal peoples of the City of Greater Sudbury. Aboriginal people have contributed to evidence-based research and knowledge translation. Developing health promotion materials for Indigenous populations requires evidence-informed approaches. While it is desirable to have tools and resources that can be shared nationally, it is important that those tools leave room for local adaptation in order to improve uptake and effectiveness. Culturally-grounded tools hold the potential to improve dementia health literacy and increase cultural safety.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".