Building knowledge development and exchange capacity in Canada: lessons from Youth Excel
Bibliographic record
Abstract
INTRODUCTION: Youth Excel was a 3-year pan-Canadian initiative to advance youth health through improving knowledge development and exchange (KDE) capacity. KDE capacity refers to an improvement cycle linking evidence and action. Capacities include local surveillance of youth behaviours; knowledge exchange; skills, resources and a supportive environment to use knowledge; and evaluation. METHODS: Interviews were conducted with Youth Excel members, including 7 provincial teams and 2 national organizations. Interviews explored participant experiences with building KDE capacity. RESULTS: Local surveillance systems were considered the backbone to KDE capacity, strengthened by co-ordinating surveys within and across jurisdictions and using common indicators and measures. The most effective knowledge exchange included tailored products and opportunities for dialogue and action planning. Evaluation is the least developed KDE component. Building KDE capacity requires frequent dialogue, mutually beneficial partnerships and trust. It also requires attention to language, vision, strategic leadership and funding. CONCLUSION: Youth Excel reinforces the need for a KDE system to improve youth health that will require new perspectives and sustained commitment from individual champions and relevant organizations.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| 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".