Researchers Supporting Schools to Improve Health: Influential Factors and Outcomes of Knowledge Brokering in the COMPASS Study
Bibliographic record
Abstract
BACKGROUND: Although schools are considered opportune settings for youth health interventions, a gap between school health research and practice exists. COMPASS, a longitudinal study of Ontario and Alberta secondary students and schools (2012-2021), used integrated knowledge translation to enhance schools' uptake of research findings. Schools received annual summaries of their students' health behaviors and suggestions for action, and were linked with COMPASS knowledge brokers to support them in making changes to improve student health. This research examines the factors that influenced schools' participation in knowledge brokering and associated outcomes. METHODS: School- and student-level data from the first 3 years of the COMPASS study (2012-2013; 2013-2014; 2014-2015) were used to examine factors that influenced knowledge brokering participation, school-level changes, and school-aggregated student health behaviors. RESULTS: Both school characteristics and study-related factors influenced schools' participation in knowledge brokering. Knowledge brokering participation was significantly associated with school-level changes related to healthy eating, physical activity, and tobacco programming, but the impact of those changes was not evident at the aggregate student level. CONCLUSIONS: Knowledge brokering provided a platform for collaboration between researchers and school practitioners, and led to school-level changes. These findings can inform future researcher-school practitioner partnerships to ultimately enhance student 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 | 0.042 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".