Report summary – Prevalence and monetary costs of dementia in Canada (2016): a report by the Alzheimer Society of Canada
Bibliographic record
Abstract
Dementia prevalence estimates vary among population-based studies, depending on the definitions of dementia, methodologies and data sources and types of costs they use. A common approach is needed to avoid confusion and increase public and stakeholder confidence in the estimates. Since 1994, five major studies have yielded widely differing estimates of dementia prevalence and monetary costs of dementia in Canada. These studies variously estimated the prevalence of dementia for the year 2011 as low as 340 170 and as high as 747 000. The main reason for this difference was that mild cognitive impairment (MCI) was not consistently included in the projections. The estimated monetary costs of dementia for the same year also varied, from $910 million to $33 billion. This discrepancy is largely due to three factors: (1) the lack of agreed-upon methods for estimating financial costs; (2) the unavailability of prevalence estimates for the various stages of dementia (mild, moderate and severe), which directly affect the amount of money spent; and (3) the absence of tools to measure direct, indirect and intangible costs more accurately. Given the increasing challenges of dementia in Canada and around the globe, reconciling these differences is critical for developing standards to generate reliable information for public consumption and to shape public policy and service development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".