Beyond the Youth Athlete: Exploring Concussion Education and the Provision of Social Support
Bibliographic record
Abstract
Youth can experience a concussion through many mechanisms including motor vehicle collisions, sports, falls, recreational play, assault and other causes. Despite the many mechanisms of sustaining a concussion, to date, most concussion education has been provided to athletes or within sport settings and focused on concussion reporting behaviours. Following a concussion, youth can experience a range of concussion symptoms that are cognitive, physical, sleep and emotional/behavioural, with these symptoms impacting youths’ ability to meaningfully participate in activities. Specifically, the influence of emotional symptoms on participation has been highlighted within the literature and the provision of social support has been suggested as one way to decrease these symptoms. Therefore, the motivation for this thesis is to inform concussion education interventions for all youth (athletes and non-athletes) in order to promote positive concussion-related behaviours and health outcomes. To do so, this thesis will explore: (1) existing concussion education and (2) youths’ intentions to provide social support to a peer with a concussion. This thesis is comprised of four studies. Study 1 identifies online concussion resources that are up-to-date, accurate and designed for Canadian youth by assessing the resource content as well as the readability, usability and suitability. Study 2 involves the completion of a scoping review that explores peer-reviewed concussion education delivered in the school setting. This scoping review included 27 studies and focused on the education context, delivery method, development and evaluation. Study 3 and 4 highlight relationships between demographic variables, concussion knowledge and social support-related constructs, and identify youth who may be more likely to provide social support. Study 3 involves the delivery of a novel concussion survey based on the Theory of Planned Behaviour to 200 youth (athletes and non-athletes). Study 4 builds on preliminary work conducted in Study 3 by delivering a shortened and more feasible version of the survey to 1330 Canadian high school students. The findings from this thesis highlight the opportunity to develop concussion education that fills existing gaps by delivering education to all youth and including content specific to the provision of social support to youth with a concussion.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".