P.077 A Concussion-U educational presentation improves knowledge and attitudes of concussion amongst elite female high-school hockey players
Bibliographic record
Abstract
Background: Research has suggested that female athletes have a higher incidence of concussion compared to their male counterparts. As such, programs designed to improve knowledge and attitudes of concussion should target this high-risk population. Previous work demonstrated the effect of a novel Concussion-U educational presentation on knowledge and attitudes of concussion amongst male Bantam and Midget AAA hockey players. The objective of this study was to determine if the same presentation was effective in improving the knowledge and attitudes of concussion in a cohort of elite female hockey players. Methods: 26 elite female high-school aged (14-17) hockey players from the province of New Brunswick consented to participate in the study. Each participant completed a modified version of Rosenbaum and Arnett’s Concussion Knowledge and Attitudes Survey questionnaire immediately before and after a Concussion-U educational presentation. Results were compared across the two time-points to assess the effectiveness of the presentation. Results: Concussion knowledge and attitude scores significantly (p<.001) increased from pre-presentation to post-presentation by 12.5% and 13.4%, respectively. Conclusions: A Concussion-U educational presentation resulted in increased knowledge and improved attitudes towards concussion in elite female hockey players. Future research should examine the long-term retention of these improvements.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".