Bibliographic record
Abstract
BACKGROUND: It is not clear how best to coordinate, facilitate, and catalyze dementia research at the national level of most countries. METHOD: Literature search on CCNA and qualitative review of website of CCNA, newsletters, and grant renewals in Canada. RESULT: The Canadian Consortium on Neurodegeneration in Aging (CCNA) was created by the Canadian federal government in 2014 through the Canadian Institutes for Health Research (CIHR). Two five-year funding cycles have occurred following peer review, and a third cycle (Phase 3) has just begun. Twenty national teams were established, with research topics focussing on national research strengths, spanning from basic to clinical science to health resource systems. Teams have facilitated greater interaction. Responding to the needs of researchers within the CCNA teams, a unique sample of 1,173 deeply phenotyped patients with various forms of dementia was accrued and studied over eight years (COMPASS-ND). In the second phase of funding (2019-2024), a national dementia prevention research program (CAN-THUMBS UP) was set up. In a short time, this prevention program became a member of the World Wide FINGERS prevention consortium. Cross-cutting programs were established to support the enterprise, focussing on KT, Training, and Sex and Gender in Dementia. A unique group integrated persons with lived experience into the national research program (EPLED, Engagement of People with Lived Experience of Dementia). An emphasis was placed on developing knowledge and capacity and procedures for investigating dementia in the Canadian Indigenous communities, where it is higher than other populations. Objective measures have demonstrated increased synergy and productivity among Canadian dementia researchers since establishment of CCNA, along with leveraging of new grants equal to the CIHR funding. More than 600 journal articles have resulted from CCNA work, with higher impact than corresponding non-CCNA work. The network has had demonstrable impact on policy-makers and been a conduit towards greater impact of the research community nationally and internationally. CONCLUSION: Enhancement of synergy and networking have contributed to the considerable success of CCNA by all measures. CCNA is evidence that an organized "centrally-organized" approach to dementia research can catalyze important progress nationally and yield significant and measurable results.
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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 | 0.069 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.029 | 0.038 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".