A mixed-method investigation of a concussion education intervention for high school athletes
Bibliographic record
Abstract
Although experts have noted that adolescent athletes should be educated about concussions to improve their safety (Broglio et al., 2014), there is no agreement on the most effective strategy to disseminate concussion education (Caron, Bloom, Falcão, & Sweet, 2015). The purpose of our study was to create, deliver, and assess a concussion education intervention for high school athletes (N = 35, Mage = 15.94; SD = 0.34). The intervention consisted of four in-person presentations that were designed to improve participants' concussion knowledge (CK) and attitudes (CA). Quantitative data were gathered using the Rosenbaum Concussion Knowledge and Attitudes Survey at pre, post, and 2 months post-intervention to measure changes in CK and CA. Qualitative data were collected using focus group interviews approximately two weeks following the concussion education intervention. Significant pre-post differences in participants' CK scores were found (t = -2.000, p = .000, d = -.884), as well as from pre to 2 months post-intervention (t = -1.971, p = .000, d = -.831). Thematic analysis of the focus group data revealed the participants acquired CK about the role of protective equipment and symptom variability, and specific to CA, they intended to avoid dangerous in-game collisions. Our study was the first to create and deliver a concussion education intervention across multiple time-points, and to use mixed-methods in its assessment. Our results contribute to a limited body of research on concussion education interventions and highlight a need to continue exploring strategies to enhance athletes' health and well-being in the sport setting.Acknowledgments: The authors would like to acknowledge the Bloomberg-Manulife doctoral fellowship for supporting this research
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".