USING THE SCHOOL HEALTH ACTION, PLANNING AND EVALUATION SYSTEM TO INFORM EVIDENCE-BASED PREVENTION PROGRAMMING DECISION-MAKING IN SCHOOLS
Bibliographic record
Abstract
Objectives Tobacco use, physical activity and obesity are important paediatric health issues. Programme providers responsible for these areas need evidence to guide their intervention choices within the school and community setting. Methods The school health action, planning and evaluation system (SHAPES) is a data collection and feedback system designed to support population-based intervention planning, evaluation and field research related to youth. The tobacco use and physical activity modules of SHAPES both consist of three elements: (1) a machine-readable questionnaire to support the collection of relevant behavioural data from all students (grades 6 to 12) in a school; (2) a school administrator questionnaire to assess school policies, programmes and resources related to student behaviour and (3) a school-specific computer-generated feedback report documenting both student behaviours and the availability of school programmes and policies. Results SHAPES has created a more innovative linkage between research and practice by providing stakeholders with the evidence they need, when they need it, in a context-specific form that is useful and understandable for guiding and evaluating prevention programming. The demand for SHAPES is high; these tools have been completed by over 350 000 students in more than 700 schools in Canada since 2000. Conclusions SHAPES has been successful in: engaging local health and education systems in planning, tailoring and evaluating school health initiatives based on evidence; engaging researchers to assess contextual influences on youth behaviour and providing a platform to study the processes and structures required for effective knowledge transfer and exchange in school settings.
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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 | 0.154 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".