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Record W7161982724 · doi:10.82308/46890

The development and implementation of a concussion education program for high school student-athletes

2016· dissertation· en· W7161982724 on OpenAlexaboutno aff
Jeffrey Caron

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsConcussionAthletesPsychological interventionQualitative researchSuicide preventionInjury preventionHuman factors and ergonomicsPoison control

Abstract

fetched live from OpenAlex

Concussions are the most frequently occurring brain injury in sports (Carroll & Rosner, 2012), and the injury can be particularly problematic for adolescent athletes because of their developing brains (Carman et al., 2015). As a result, experts have highlighted the importance of improving athletes' knowledge of concussions to improve their health and safety (McCrory et al., 2013). Despite this, there is no consensus regarding the most effective way to disseminate concussion knowledge (Mrazik et al., 2015). The purpose of this dissertation was to develop and implement a concussion education program for high school student-athletes in Eastern Canada. This was accomplished through a cohesive series of four original manuscripts. The first manuscript was a review paper that investigated literature on concussion education programs. Results revealed there have been three types of concussion education programs to date: interactive oral presentations, educational videos, and computer-based learning programs. However, these interventions have been limited by the dissemination of knowledge at one time-point only and by minimally implementing qualitative methods. The second manuscript was a qualitative study that explored high school coaches' insights and perceptions of concussions. The coaches said they taught athletes skills during practices and games to improve their safety and well-being, which they said they valued more than winning championships. Coaches also mentioned that some of their athletes were occasionally disingenuous when reporting concussion symptoms. The third manuscript was a qualitative study that gathered high school athletes' insights on concussions, including the mediums through which they acquired information about the injury. Results revealed the high school athletes primarily acquired information about concussions through interactions with peers and family members, media reports involving professional athletes, and school projects. Additionally, the athletes noted they attempted to deceive coaches and health professionals about concussions, a finding that was related to the coaches' perceptions in study two. Results from the first three manuscripts were used to create a concussion education program for high school athletes. Thus, the fourth manuscript investigated a concussion education program for high school athletes, which consisted of four interactive oral presentations that were evaluated using a mixed method design. The results revealed improvements in participants' knowledge of concussions after exposure to the concussion education program. Participants also indicated the concussion education program might influence their future in-game behaviors, such as avoiding dangerous collisions. Furthermore, the athletes said they enjoyed the interactive nature of the presentations and the use of case study examples. In sum, this dissertation contributes to the research and practice of concussion education in many ways. To our knowledge, it is the first program of research to systematically develop and implement a concussion education program for high school athletes. Results from this dissertation indicated that interactive oral presentations were an effective strategy to disseminate concussion information to high school athletes, and provides recommendations for future research in this still underdeveloped domain.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.035
GPT teacher head0.450
Teacher spread0.415 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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