Developing a knowledge exchange tool for school- based health policies and programs
Bibliographic record
Abstract
Youth smoking and physical inactivity are significant public health issues, with implications for both health and education stakeholders, as school-based policies and programs have the potential to reach a broad population of youth to address these issues. Knowledge exchange tools designed around comprehensive school-level data collection systems allow for dissemination of evidence into such policies and programs. The purpose of this manuscript is to describe the process of developing knowledge exchange feedback reports for school-based health policies and programs, using the School Health Action, Planning and Evaluation System (SHAPES) data collection system. SHAPES-Ontario is a project that utilized the SHAPES research platform to collect student-level behavioural data and school-level policy and programs data on tobacco and physical activity in 81 secondary schools across Ontario, Canada. Methods used to develop the feedback reports involved categorizing and scoring survey response options based on extensive research evidence and expert feedback. Feedback report scores were aggregated into overall grades and presented in a short and long version of a feedback report for school administrators. These reports present prime examples of how to use the principles of knowledge exchange in developing a tool to bridge the gap between research and practice. Keywords: Secondary schools, health policies, tobacco, smoking, physical activity
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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 | 0.075 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".