\nLessons learned through a pan-Canadian engagement of policy makers, practitioners and researchers focused on youth health: The Youth Excel CLASP approach \n
Bibliographic record
Abstract
This session will provide participants with the opportunity to reflect and discuss lessons learned from an initiative called Youth Health Collaborative: ‘Excelerating’ evidence-informed action (Youth Excel), that aimed to better understand and build capacities for community monitoring and knowledge exchange to advance youth health goals. The project had seven provincial partners (BC, AB, MB, ON, NB, NL, PE) and two national partners (Pan-Canadian Joint Consortium for School Health (JCSH), with the Propel Centre for Population Health Impact at the University of Waterloo undertaking the role of secretariat. Youth Excel’s initial vision is to: as part of routine practice, all federal, provincial, and territorial jurisdictions in Canada convene leaders in policy, practice, research, evaluation and youth themselves, to jointly set priorities a) for action (i.e., to determine what interventions are most promising) and b) to learn from action (i.e., to determine which individual interventions or mix of interventions will be studied formally). Collective, participatory agenda setting was through provincial and national forums and peer learning was evident across jurisdictions. New models for Knowledge Development and Exchange were developed in three ‘case-study’ provinces and Core Indicators and Measures (CIM) were developed for physical activity, tobacco control and healthy eating. In order to evaluate the effectiveness of this approach, 12 interviews were conducted with policy makers, practitioners and researchers and the data were analyzed using a multi-stage thematic approach. Findings indicated that success is predicated on building trust, paying attention to language, strategic leadership, vision and funding. It was also evident that considerable work needs to be done to advance critical issues of youth health.
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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 | 0.027 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".