Pros and Cons of 19 Sport-Related Concussion Educational Resources in Canada: Avenues for Better Care and Prevention
Bibliographic record
Abstract
Introduction: While progress is occurring regarding the diagnosis and treatment of concussion, more work is required on how to translate new knowledge about concussion to stakeholders. The purpose of the present study was to identify the organizations who should deliver sports-related concussion information, the best methods for delivery, and factors affecting the accessibility and usability of the resources for knowledge translation (KTR). Methods: Nationwide electronic survey of the Canadian sports community regarding concussion and knowledge translation. Results: A total of 12168 usable responses were obtained. National or provincial sports organizations, coaches and trainers, federal and provincial governments were identified as the top five groups who should deliver concussion information regardless of the respondent’s age or community role. Among the information delivery options, YouTube videos and TV segments were most selected. Social media were more popular among younger respondents whereas brochures were more popular among those over 35 years of age. Usability and accessibility of KTR varied widely. Regression analyses showed that sex and community/social role of the respondents affected respondents’ rating of the accessibility and usability. Conclusion: Sports organizations, schools, and, governments should play larger roles in the delivery of concussion information through teams, leagues, school physical education class, TV, online or social media, brochures, or coaches/trainers. Respondents’ ratings of the accessibility and usability of the various concussion KTR will provide useful information for both the KTR developers and users.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".