THE MANY FACES OF DEMENTIA IN CANADA: EXAMINING SEX, ETHNICITY, AND AGE OF ONSET IN THE LANDMARK STUDY
Bibliographic record
Abstract
Abstract With the rapidly increasing size of the dementia population, it is important to enhance our understanding of the similarities and differences that are found across the people living with condition. This work takes a closer look at the many faces of dementia in Canada by highlighting the diversity that is found within the national population projections for dementia. To conduct these forecasts, a simulation model was developed using demographic characteristics and risk factors for dementia to estimate the numbers of people living with dementia in Canada from 2020-2050. From the results of the Landmark Study, we will highlight findings related to sex, Indigenous Peoples, ethnic origins, and young-onset dementia. In 2020, 61.3% of dementia diagnoses were in females and this sex ratio is projected to stay constant over the three decades. For Indigenous Peoples of Canada, dementia numbers are estimated to increase by 273% (2020: 10,800; 2050: 40,300). People of Asian origin will increase from 8% of people living with dementia in 2020 to 24% by 2050. The changes are driven both by future immigration, and by people who have already immigrated to Canada in the past few decades. The Landmark Study also projects that there could be over 40,000 people under the age of 65 living with dementia in Canada by 2050.These findings illustrate the changing landscape of people living with dementia in Canada. These demographic characteristics, as well as other distinctions across population groups, profoundly affect the way in which dementia is experienced and their care needs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".