A SAMPLE OF QUALITY OF LIFE RESEARCH FROM THE CANADIAN CONSORTIUM ON NEURODEGENERATION IN AGING
Bibliographic record
Abstract
The Canadian Consortium on Neurodegeneration in Aging (CCNA) provides the infrastructure and support that facilitates collaboration amongst Canada’s top dementia researchers. By accelerating the discovery, innovation, and the adoption of new knowledge, the CCNA positions Canada as a global leader in increasing understanding of neurodegenerative diseases, working in the areas of prevention, treatment and quality of life. The goal of the CCNA is to offer a collaborative and synergistic space that attracts the best researchers in the field. Together, researchers work on transformative ideas to positively impact the quality of life and the quality of services for individuals living with neurodegenerative diseases. In fact, seven research teams are dedicated to one of CCNA’s overarching themes, quality of life. This symposium will focus on showcasing the work of 5 teams within the Quality of Life theme all of whom focus on persons with dementia. Specifically, researchers from Team 16 will share a driving cessation decision-making and coping framework and toolkit. Team 17 will describe how dual sensory and cognitive loss influence every day functioning and quality of life. Team 18 will discuss a group psychotherapy intervention to support dementia family carers in managing work-life conflicts. Team 19 will present the impact of primary care interventions on the knowledge attitudes and practices of clinicians and their association with quality of dementia care. Finally, Team 20 will discuss the implementation of community based participatory action research to improve approaches to dementia care in Indigenous populations in Canada. .
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".