Perfecting the scramble: Examining the influence of the COVID-19 pandemic on youth Canadian tackle football
Bibliographic record
Abstract
This thesis contains two projects that focus on the impact of the COVID-19 pandemic on youth tackle football, namely baseline concussion testing, head impacts, and injuries. The first study provided baseline reference scores on the SCAT5 concussion assessment tool and aimed to examine if age, concussion history, and self-reported medical conditions are associated with SCAT5 subcomponent performance in youth football participants (age 13-18). Due to social distancing regulations, SCAT5 assessments were performed virtually using the Zoom video platform. SCAT5 assessments were administered at baseline with 537 youth Canadian tackle football participants. Age and concussion history were not found to be associated with SCAT5 subcomponent scores. Participants with self-reported medical diagnoses (e.g., ADHD, depression) performed poorer on SCAT5 subcomponents and had higher symptom severity scores. The second study examined head impacts and suspected injuries (non-concussion and concussion) in youth football using video analysis. Games were videotaped for two Bantam football (ages 13-15) seasons including a 12-on-12 traditional format (with playoffs) in 2021 and a modified 2020 season that reduced the number of on-field players to 9-on-9 with reduced field width. Head impacts and suspected injuries were identified and tagged according to team unit (i.e., offense, defense, kicking team, receiving team). Head impacts and suspected injury and concussion were expressed as rates per 100 player-plays and per 10 gameplay minutes. Head impact and suspected injury rates did not differ between the 9-on-9 and 12-on-12 format, but the offense experienced significantly more head impacts in 12-on-12 playoff versus 12-on-12 regular season games (IRRplays: 1.33; 95% CI: 1.07-1.65; IRRmins: 1.26; 95% CI: 1.03-1.56). These findings along with future research may contribute to policy changes that have the potential to improve player safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".