Can knowledge exchange support the implementation of a health-promoting schools approach? Perceived outcomes of knowledge exchange in the COMPASS study
Bibliographic record
Abstract
BACKGROUND: Despite the potential population-level impact of a health-promoting schools approach, schools face challenges in implementation, indicating a gap between school health research and practice. Knowledge exchange provides an opportunity to reduce this gap; however, there has been limited evaluation of these initiatives. This research explored researchers' and knowledge users' perceptions of outcomes associated with a knowledge exchange initiative within COMPASS, a longitudinal study of Canadian secondary students and schools. Schools received annual tailored summaries of their students' health behaviours and suggestions for action and were linked with knowledge brokers to support them in taking action to improve student health. METHODS: Qualitative semi-structured interviews were conducted with COMPASS researchers (n = 13), school staff (n = 13), and public health stakeholders (n = 4) to explore their experiences with COMPASS knowledge exchange. Key issues included how knowledge users used school-specific findings, perceived outcomes of knowledge exchange, and suggestions for change. RESULTS: Outcomes for both knowledge users and researchers were identified; interestingly, knowledge users attributed more outcomes to using school-specific findings than knowledge brokering. School and public health participants indicated school-specific findings informed their programming and planning. Importantly, knowledge exchange provided a platform for partnerships between researchers, schools, and public health units. Knowledge brokering allowed researchers to gain feedback from knowledge users to enhance the study and a better understanding of the school environment. Interestingly, COMPASS knowledge exchange outcomes aligned with Samdal and Rowling's eight theory-driven implementation components for health-promoting schools. Hence, knowledge exchange may provide a mechanism to help schools implement a health-promoting schools approach. CONCLUSIONS: This research contributes to the limited literature regarding outcomes of knowledge brokering in public health and knowledge exchange in school health research. However, since not all schools engaged in knowledge brokering, and not all schools that engaged discussed these outcomes, further research is needed to determine the amount of engagement required for change and examine the process of COMPASS knowledge brokering to consider how to increase school engagement.
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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 | 0.030 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".