Concussion Public Policy in Elementary and High Schools in Ontario, Canada: A Cross‐Sectional Survey to Examine Implementation Compliance, Barriers, and Facilitators
Bibliographic record
Abstract
BACKGROUND: Concussion public policies have been developed to address the burden of concussions. The aim of the present study was to examine implementation compliance, barriers, and facilitators of Canada's first concussion public policy, Ontario's Policy/Program Memorandum 158: School Board Policies on Concussion (PPM158). METHODS: An electronic survey was sent to 515 randomly selected elementary and high school principals across specific geographic, language, and publicly funded school types in Ontario. Data were analyzed using both qualitative and quantitative methods. RESULTS: One hundred and thirty-five principals responded to the survey (26%). Concussion education was provided to teachers in 81% of schools, to students in 83%, and coaches in 79%. Additionally, 89% reported having a return-to-learn protocol in place and 90% reported having a return-to-play protocol. Implementation barriers included difficulties in providing concussion education to parents (42%), obtaining notes from physicians, and maintaining the volume of documentation. Eighty-seven percent of respondents believed that PPM158 improves student well-being. CONCLUSIONS: Identified implementation barriers and facilitators can inform concussion policy practices to improve student well-being. We recommend: (1) an appointed concussion policy lead at each school, (2) electronic documentation, (3) determining the optimal education format to improve parent/guardian education, (4) fostering relationships between schools and health care professionals, and (5) student concussion education in every grade in Ontario schools.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".