AQUILA - Improving the quality of life for older adults living with a neurodegenerative disease through the study of the scaling up of knowledge tools supporting care transitions within francophone communities in Canada
Bibliographic record
Abstract
Canadian studies report that linguistic barriers, such as those experienced by Francophones in a minority setting, can contribute to inadequate patient assessment, diagnostic errors or treatment delays, an incomplete understanding of the patient's condition and treatment, and a loss of trust in the care and services received. In a context where older adults living with a neurodegenerative disease (NDD) are often subjected to poor quality care transitions, there is an urgent need to better integrate all these factors with a focus on language and culture. This integration is necessary to promote and support their engagement in the care transitions that concern them. The overarching AQUILA program aims to scale up knowledge tools that support optimal care transitions throughout their life trajectory. To achieve this, the initial phase involves a systematic mapping and a Delphi study. Crucially, the next phase will involve implementing the prioritized tools via reflexive practice workshops in three Francophone healthcare settings in minority contexts across Canada. Ultimately, this will contribute to improving the quality of life for older adults living with an NDD and their caregivers, thus fostering health equity.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".