Concussion Education in the School Setting: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Concussions are a prevalent injury among youth, and concussion education has the potential to promote positive concussion-related behaviors. Recent recommendations and legislation have increased concussion education provided in schools; however, little is known about the education context, delivery method, development, and evaluation. A scoping review was conducted to identify peer-reviewed literature on concussion education delivered in the school setting. METHODS: Six databases were searched (MEDLINE, CINAHL, EMBASE, PsycINFO, SPORTDiscus, and ERIC) to identify published articles from 2002 to July 16, 2020 that delivered concussion education in the school setting. Included studies described the concussion education and were written in English. RESULTS: A total of 11,373 articles were identified and screened, with 27 studies meeting eligibility criteria and therefore, included. The studies delivered education to various stakeholders including students (n = 12; 44.4%), coaches (n = 5; 18.5%), educators (n = 3; 11.1%), parents (n = 1; 3.7%), and a mixed audience (n = 6; 22.2%). The education format varied and six studies (22.2%) developed the education based on a theory, model, or framework. CONCLUSIONS: This study found substantial variability in the context, delivery method, development, and evaluation of education delivered in schools and further evaluation of this education is needed to ensure it is best-suited for school-based stakeholders.
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".