Sport-Related Concussion Knowledge in French-Speaking Athletes, Coaches, and Health Care Professionals: Results From an International Survey on 2073 Participants
Bibliographic record
Abstract
OBJECTIVE: Evaluating sports-related concussion (SRC) knowledge, awareness of evaluation tools and management protocols, and access to educational resources within the French-speaking sports community in Canada and Europe. DESIGN: Cross-sectional, anonymous online survey. PARTICIPANTS: French-speaking athletes, coaches, and health care professionals (HCPs). INTERVENTION: The electronic survey consisted of 33 questions that can be grouped into the following 3 categories: (1) SRC knowledge assessment through self-report and general knowledge questions, (2) awareness of internationally recommended detection tools and managements protocols, and (3) access to educational resources. The survey was codeveloped with athletes, coaches, and HCP and validated by and expert panel. MAIN OUTCOME MEASURES: A SRC knowledge score was built based on answers to 4 questions on SRC mechanisms, symptoms, and management. Awareness of the Sport Concussion Assessment Tool 5, the Concussion Recognition Tool 5, and the 6-step return to sport protocol were collected with coaches and HCPs. RESULTS: Overall, 2073 participants responded to the survey, comprising 48% athletes, 33% coaches and 19% HCP. Knowledge scores (/100, median [IQR]) were highest among HCPs [98.1 (96.2, 100)], followed by coaches [96.2 (73.1, 98.1)], and athletes [90.4 (67.3, 92.3)] with significant differences between the groups ( P < 0.001). Participants from Canada demonstrated higher knowledge scores [92.3 (75.0, 98.1)] than those from Europe [75.0 (67.3, 96.2); P < 0.001]. Similar differences were observed for the awareness of recommended tools and the access to educational resources. CONCLUSIONS: Knowledge translation strategies should be adapted to better reach the French-speaking sports community in Europe and further individualized according to professional role.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".