Factors affecting the concussion knowledge of athletes, parents, coaches, and medical professionals
Bibliographic record
Abstract
OBJECTIVES: To determine the predictors of knowledge and awareness of concussion symptoms and outcomes through a survey of athletes, parents of players and coaches in sports settings in Canada. METHODS: A cross-sectional survey of athletic communities in Canada was conducted. Respondents' concussion knowledge score consists of responses to questions about the symptoms, diagnosis, and treatment of a concussion and the timing of return-to-sport post-concussion. The percentage of correct responses was defined as the "identification rate." The extent to which participant factors affected the scores was examined by univariate and multivariate analyses. RESULTS: Respondents were able to identify a mean of 80.6% of symptoms. Cognitive symptoms were most commonly known, and mental health symptoms associated with concussion were least known, and health professionals, coaches, and those with a personal history of concussion had the highest levels of overall knowledge. Language, age, educational level, annual household income, and traumatic brain injury history were good predictors of better concussion knowledge. CONCLUSION: Those designing and implementing interventions aimed at concussion management and prevention should ensure that younger, lower income, lower educational, non-English-speaking persons, and those without experience of traumatic brain injury or concussion be specifically accounted for in the design and implementation of interventions to prevent and treat concussion and mild traumatic brain injury.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".