Football athletes' knowledge of concussion: A 10-year follow-up
Bibliographic record
Abstract
Despite the highly publicized and potentially severe consequences of concussion, athletes continue to possess many inaccuracies in concussion-related knowledge. This leads to the presumption that improving athletes' knowledge of concussion is necessary for any injury prevention program. The purpose of this study was to examine the concussion knowledge of CIS football athletes and to compare these findings to those obtained from a similar sample 10 years earlier. A convenience sample of 44 athletes (1999) and 55 athletes (2009) completed the Concussion Questionnaire which examines knowledge of concussion in four categories: Neurologic, 8 items examining knowledge of concussion and the brain; Equipment, 3 items looking at the role of protective equipment (e.g., helmet, cage, mouth guard) in preventing concussion; Recovery, 5 items examining recovery from concussion; and Signs and Symptoms, 9 items looking at knowledge of concussion signs and symptoms. Athletes were asked whether they believed each statement was Definitely True, Probably True, Probably False, or Definitely False. When examining the overall accuracy of the responses, athletes received a grade of F (less than 50% correct) for every category in both years studied. Importantly, the 2009 sample's overall accuracy score in the Neurologic category decreased from 47% to 38% (p < .01). It is clear from these data that education programs regarding concussion are critically needed for this extremely vulnerable population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".