Health behaviours and dementia literacy among Indigenous older adults during COVID-19
Bibliographic record
Abstract
Globally, there is a much higher prevalence of dementia in Indigenous peoples compared to non-Indigenous populations. Moreover, Indigenous older adults can experience an earlier onset of dementia by up to 10 years compared to their non-Indigenous counterparts. COVID-19 has negatively impacted many health behaviours that are risk factors for dementia, including reducing physical activity, worsening diet, and increasing sedentary time, among others. However, research has yet to examine whether COVID-19 has negatively impacted the health behaviours of Indigenous older adults specifically. Furthermore, it is not currently known whether Indigenous older adults are aware of the health behaviours, among other factors, that are known risk factors of dementia. This study aims to examine the health behaviours of Canadian Indigenous older adults during COVID-19, compared to before COVID-19, and assess their knowledge of dementia risk factors. The study design and methodology consist of mixed methods and survey research. The online questionnaire combines validated questionnaires in their entirety as well as specific questions pulled from additional validated questionnaires. Interview questions include a fully PAGE 8 validated questionnaire, as well as questions created based on previous qualitative studies that were literature-informed. Findings from the study may help inform areas for improved education in Indigenous older adults on the topic of dementia and determine how resources that promote healthy active living may be improved in this population during the COVID-19 pandemic and afterwards.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".