Concussion education: a randomised trial with undergraduate students
Bibliographic record
Abstract
Objective To evaluate optimal methods for educating individuals about concussions. Design Prospective randomised controlled trial. Setting University setting. Participants 162 undergraduate students. Intervention Students from a university participant pool were randomly assigned to one of three conditions: 1) control group (CN); 2) internet group (IG); 3) presentation group (PG). All subjects completed the knowledge concussion questionnaire (18 questions) in the concussion knowledge section of the questionnaire published by Rosenbaum & Arnett (2014). Subjects completed a pretest and a posttest. The IG was provided with 3 websites and given 30 minutes to review this material. The PG group was involved in a 45 minute interactive lecture from a neuropsychologist. Main outcome measure Concussion knowledge. Main results A repeated measures ANOVA suggested a significant interaction between group and time F (2, 159)=30.2, p<0.001. The PG demonstrated significantly higher scores at posttest compared with both the IG and CN groups [(F (2, 159)=12.6, p<0.001 although all groups presented with improved scores at the posttest interval compared with the pretest. Post-hoc pairwise comparison at posttest interval between CN and IG groups did not reach statistical significance (p=0.50). Specific items suggested inaccurate information about concussions may be common in the undergraduate population. Conclusions There are many methods used to educate athletes about concussions. As expected, an interactive presentation about concussions was more effective at improving concussion knowledge than reviewing information from the internet. Results also suggested the importance of clarifying existing myths about concussions Conflicts There were no conflicts to declare. Competing interests None.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".