Concussion (mis)education: Implications of prevention narratives on youth athlete concussion experiences
Bibliographic record
Abstract
In recent years, there has a considerable uptake in research exploring youth athlete experiences with sport-related concussion (SRC) education, particularly concerning media coverage (Kita et al., 2020), credible information sources (Mallory et al., 2020), and concussion recovery (Bridel et al., 2020). Notably, as Mrazik et al. (2015) describe, “more knowledge and information, particularly printed literature, does not equate with behaviour change” (p.1). Indeed, there are numerous factors which impact an individual’s knowledge uptake, regardless of the medium in which the knowledge is presented (Cusimano et al., 2017). Despite this uncertainty, education has assumed the role of a primary approach to injury prevention. This role, we argue, must be critically assessed and problematized. Specifically, we examine the repercussions of some of the most dominant forms of concussion education–those that are as static, sensationalized, and disconnected from sociocultural implications of sport participation–and the subsequent uptake and impact of this information by/on youth athletes. To do so, we draw on research that involved semi-structured interviews with youth athletes (N=28; aged 13-18-years-old) focused on understanding experiences with concussion knowledge and education. We highlight three important domains related to athletes’ experiences with concussion education concerning (1) sufficient education, (2) scare tactics in education efforts, and (3) equity, access, and responsibility. By problematizing education as an effective mode of injury prevention, we draw attention to a gap within current SRC literature concerning the intersection of education, knowledge and behaviour with the social and cultural realities of concussion experiences.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".