Experience with canada’s first school-based concussion policy, and overall evaluation using a modified delphi methods
Bibliographic record
Abstract
Objective Ontario is the first province in Canada to implement a school-based concussion policy, Policy/Program Memorandum (PPM) 158. It requires all public school boards to develop a concussion strategy. The aim of this research was to conduct a pilot assessment of PPM 158 implementation in one large school board, and then to develop a novel policy evaluation tool to evaluate how well all 72 school board concussion strategies align with best practices identified through a modified Delphi method. Design Descriptive. Setting Ontario public school boards (n=72). Participants and main results In the pilot assessing PPM 158 implementation, there were 39 respondents of 109 high school principals in the Toronto District School Board (TDSB). 92% of schools provided education to students in the TDSB, and fewer educated staff (85%) or parents (43.6%). Most schools had return-to-play protocols (92%) and fewer had return-to-learn protocols (77%). Outcome measures As a result of the pilot study, it was decided to develop the following outcome measures: stakeholder concussion education for staff, parents and students; mode of education; and existence of return-to-play and return-to-learn protocols. Policy evaluation will comprise the following: score achieved on the policy evaluation tool to be developed through a modified Delphi method, which reflects how each policy compares with best practices recommendations. Conclusions Significant progress has been made in developing school-based concussion in the single board assessed, but there are deficiencies in certain areas of implementation. This research has informed policies that target childhood concussions in Ontario and beyond. Competing interests None.
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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 | 0.144 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".