Confidence amid misperceptions: Problem for concussion prevention?
Bibliographic record
Abstract
Proper understanding of concussion injury is important in athletes' development of accurate risk perceptions and concussion prevention. The confidence associated with athletes' self-declared understanding may amplify misconception problems. Investigating how athlete self-declared understanding of concussion injury relates to grounded concussion experience and education can give insight into athlete's concrete and abstract understanding of concussion. We used a cross-sectional survey of 175 varsity athletes (60% male, mean age = 20) to explore athletes' grounded experience of concussion (direct / indirect experience, formal / informal education), their self-declared understanding of concussion, injury perceptions (severity, and control), and their objective understanding of concussion symptoms (accuracy). Analyses revealed significant relationships between athletes' concussion specific education, concussion history, and their injury understanding. Those who had received a concussion, had concussion specific education, or disclosed a greater number education sources, reported greater perceived understanding of concussion injury. Analyses revealed relationships between athletes' concussion understanding and their perception of the controllability of the injury through treatment and the cyclical nature of concussion. Those who reported a greater understanding of the injury perceived that concussion injury cannot be controlled with treatment and that concussion injury could be cyclical in nature. Higher perceived understanding did not relate to personal control of injury, accuracy of symptom recognition, viewing concussions as having long-term problems, or perceiving concussions to have more consequences. Athletes' perceived understanding of concussion can be at odds with the features of injury, which may lead to faulty prevention beliefs. Assessing injury beliefs is important for prevention programs.
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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 | 0.018 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".