Youth Coaches’ Perceptions And Knowledge Of Sport-related Concussions
Bibliographic record
Abstract
Coaches are community leaders and role models who are responsible for athletes’ health, well-being, and personal development (Bloom, Falcão, & Caron, 2014). Given their prominent role in athletes’ lives, it is disconcerting that coaches are underrepresented in the concussion literature. More specifically, researchers have found that youth coaches are lacking knowledge on the common signs and symptoms, management, and return to activity guidelines associated with sport-related concussions (Covassin, Elbin, & Sarmiento, 2011; White et al., 2014). PURPOSE: To understand youth coaches’ perceptions of sport-related concussions. METHODS: Individual interviews were conducted with eight youth coaches from a variety of contact and non-contact sports. All the participants reported having coached numerous athletes who suffered a concussion. Data were transcribed verbatim and analyzed using a hierarchical content analysis (Côté et al., 1993; Sparkes & Smith, 2014), a commonly used strategy in coaching science research. RESULTS: Based on the inductive analysis, two higher-order categories emerged: Athletic and Coaching Experiences and Coach Concussion Knowledge. Although the eight coaches said they were aware of concussions, the majority did not feel they had sufficient knowledge regarding concussion management, return to activity guidelines, and long-term implications. Participants said they acquired concussion knowledge through their own direct and indirect experiences as athletes and by coaching their teams. Additionally, coaches said they learned about concussions through media reports and formal concussion education training. CONCLUSIONS: Findings from this study concur with previous research that suggests knowledge emanating from peer-reviewed articles and published guidelines are not reaching coaches (White et al., 2014). This suggests that knowledge translation strategies (Straus, Tetroe, & Graham, 2013) aimed at bridging the gap between the scientific community and coaches should be considered when developing concussion education programs for these individuals. Ultimately, researchers must find the best way to educate coaches about concussions because they play an important role in young athletes’ health and well-being.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".