Association between concussion understanding and stakeholder knowledge translation in collegiate sports
Bibliographic record
Abstract
The purpose of this study was to determine if there was an association between concussion understanding and stakeholder knowledge translation in collegiate sports following the mandate of Rowan's Law in Ontario, Canada. To our knowledge, this is the first study to examine concussion knowledge translation within a sport network using social network analysis. A cross-sectional design was used to evaluate 76 collegiate athletes (54 females, 21 males, 1 not identified), aged 20.55 years (SD = 3.4) who completed a survey on sport demographics, concussion knowledge and stakeholders who provided concussion information during the sport season. Athlete concussion knowledge scores and reported stakeholders were examined. An average of three key stakeholders provided concussion information to 82% of the varsity athletes in our study. Athletes reported that a coach or athletic trainer most often provided concussion knowledge. Overall, athlete concussion knowledge scores were the same for athletes who sought concussion knowledge from stakeholders and those who did not. Over 95% of athletes in the study did not access the Rowan's Law website for mandated concussion education. These findings suggest that Rowan's Law is hugely neglected resulting in stakeholder knowledge translation having minimal influence on an athletes’ understanding of concussions. Future recommendations include verified review of mandated concussion education resources and testing of concussion knowledge for all persons associated with sport in Ontario. Due to the number of athletes seeking concussion knowledge in their varsity athlete network, accurate sport specific resources should be provided to support stakeholders who are in direct contact with athletes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".