Predictors of Collegiate Student-Athletes’ Concussion-Related Knowledge and Behaviors
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore student-athletes' concussion-related knowledge and attitudes toward reporting symptoms, demographic predictors of knowledge and attitudes, and determine whether responses to the survey changed following an online educational intervention. METHODS: A total of 108 Division I student-athletes enrolled at a large southern university completed a survey evaluating knowledge regarding concussion-related terminology, symptoms and recovery trajectories, as well as attitudes toward reporting symptoms following a possible concussion. Student-athletes completed the questionnaire both 24-48 h before and one week after reviewing the educational presentation. RESULTS: At baseline, participants correctly identified 72% of concussion symptoms included in the questionnaire, as well as correctly identified 75% of items related to the typical recovery trajectory post-concussion. A total of 54% of baseline attitudes toward reporting symptoms matched clinical best practices. Multiple analysis of variance (MANOVA) revealed that male sex and non-Caucasian race were associated with worse baseline knowledge of concussion symptoms. Concussion knowledge was not associated with attitudes toward reporting symptoms. Paired samples t-tests indicated that knowledge of concussion-related terminology improved modestly following the educational presentation. CONCLUSIONS: Some subsets of collegiate student-athletes show relatively lower knowledge about symptoms of concussion than others. As a result, these groups may benefit from increased educational efforts to ensure they recognize when a concussion may have occurred. Additionally, as knowledge and attitudes were unrelated and the intervention had a modest effect on knowledge but not attitudes, future work should explore interventions that are designed to directly alter attitudes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".