Experience with Canada’s First Policy on Concussion Education and Management in Schools
Bibliographic record
Abstract
BACKGROUND: In response to the rising incidence of concussions among children and adolescents, the province of Ontario recently introduced the Ontario Policy/Program Memorandum on Concussions (PPM No. 158) requiring school boards to develop a concussion protocol. As this is the first policy of its kind in Canada, the impact of the PPM is not yet known. METHODS: An electronic survey was sent to all high school principals in the Toronto District School Board 1 year after announcement of the PPM. Questions covered extent of student, parent, and staff concussion education along with concussion management protocols. RESULTS: Of 109 high school principals contacted, 39 responded (36%). Almost all schools provided concussion education to students (92%), with most education delivered through physical education classes. Nearly all schools had return to play (92%) and return to learn (77%) protocols. Although 85% of schools educated staff on concussions, training was aimed at individuals involved in sports/physical education. Only 43.6% of schools delivered concussion education to parents, and many principals requested additional resources in this area. CONCLUSIONS: One year after announcement of the PPM, high schools in the Toronto District School Board implemented significant student concussion education programs and management protocols. Staff training and parent education required further development. A series of recommendations are provided to aid in future concussion policy development.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| 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".