Cognitive impairment among community‐dwelling, off‐reserve indigenous populations in Canada
Bibliographic record
Abstract
Abstract Background Limited research suggests the prevalence of cognitive impairment among Indigenous populations in Canada may be higher than that among non‐Indigenous populations. This project is focused on the understudied off‐reserve Indigenous population. We used the social determinants of health model and two‐eyed seeing approach to characterize the epidemiology of cognitive impairment in Canada’s community‐dwelling, off‐reserve Indigenous population in comparison to the non‐Indigenous population using data from the Canadian Community Health Survey (CCHS). Method A Community Advisory Board was established to provide leadership, support and direction to the research. Weights were applied to generalize estimates from the sampling population to the general population. Chi‐square tests were used to compare frequencies and prevalence estimates. Risk factors for cognitive impairment will be identified using PROC GENMOD to build a multivariate logistic regression model for Indigenous and non‐Indigenous participants. Result Weighted estimates are presented for preliminary results. The prevalence of risk factors for cognitive impairment were generally higher for the Indigenous population (e.g. smoking status, level of education, food security, marital status, rural residency, heart disease, falls, diabetes, emotional health) in comparison to the non‐Indigenous population (Tables 1 & 2; p<0.01). The Indigenous population was younger than the non‐Indigenous population with nearly half of the Indigenous population being 45‐54 years of age (49% vs 38%, p<0.01). Despite the younger age profile of the Indigenous population, the overall prevalence of mild (29% vs 25%) and severe cognitive impairment (4% vs 2%) were higher for the Indigenous population compared to the non‐Indigenous population (Table 2; p<0.01). The prevalence of dementia was <1% for both study populations (p=0.33). Given the low prevalence of dementia, our bivariate and multivariable models will focus on cognitive impairment. Age‐specific cognitive impairment prevalence estimates will be reported for each age group. Odds ratios will be reported from the final multivariable model for Indigenous and non‐Indigenous participants. Conclusion This is the first study focused on cognitive impairment among the community‐dwelling, off‐reserve Indigenous population in Canada. By characterizing the epidemiology of cognitive impairment in Indigenous populations we will increase awareness of cognitive impairment and associated risk factors in off‐reserve Indigenous populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".