“Now What?” Perceived Factors Influencing Knowledge Exchange in School Health Research
Bibliographic record
Abstract
Increasing the uptake of school health research into practice is pivotal for improving adolescent health. COMPASS, a longitudinal study of Ontario and Alberta secondary students and schools (2012-2021), used a knowledge exchange process to enhance schools' use of research findings. Schools received annual summaries of their students' health behaviors and suggestions for action and were linked with a knowledge broker to support them in making changes to improve student health. The current research explored factors that influenced COMPASS knowledge exchange activities. Semistructured interviews were conducted with researchers (n = 13), school staff (n = 13), and public health stakeholders (n = 4). Interestingly, knowledge users focused more on factors that influenced their use of COMPASS findings than factors that influenced knowledge brokering. The factors identified by participants are similar to those that influence implementation of school health interventions (e.g., importance of school champions, competing priorities, inadequate resources). While knowledge exchange offers a way to reduce the gap between research and practice, schools that need the most support may not engage in knowledge exchange; hence, we must consider how to increase engagement of these schools to ultimately improve student health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.142 | 0.248 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".