Advancing Dementia Research in Canada: Early Insights From a Scoping Review of Reviews
Bibliographic record
Abstract
Abstract Recently, a national dementia strategy was developed to advance the field of brain health and dementia in Canada. However, there is a paucity of information on the current research landscape and evidence available to advance this strategy. Drawing on findings from our recent scoping review of reviews, this late breaking presentation aims to: i) identify the landscape of Canadian-led reviews on brain health and dementia research; and 2) recognize knowledge gaps and areas of innovation to strengthen brain health and dementia research, policy, and practice. Drawing on Arksey and O’Malley’s framework, a comprehensive scoping review protocol was developed which guided our search of five databases (CINAHL, PubMed, PsycINFO, Scopus, and Web of Science). The literature was independently screened by two reviewers, extracted into a standardized table, and thematically analyzed. We found a total of 7860 initial articles, with 275 reviews included in our analysis. Findings were identified in three areas: 1) risk reduction strategies related to modifiable risks, lifestyle choices, and pharmacological factors; 2) enhancing therapies and finding a cure ranging from neuroimaging techniques to biomedical research; and 3) enhancing the quality of life of people living with dementia and care partners using a range of strategies to improve mental, social, and physical aspects. Although extensive reviews exist, significant knowledge gaps remain driven by factors such as limited participant diversity, inconsistent measures, and lack of targeted coordination, among other challenges. Findings from this study reveal vital gaps and research priorities that are essential to advancing Canada’s dementia strategy forward.
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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 | 0.071 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.047 | 0.077 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".