The Impact of Impacts: Repetitive Head Impact Exposure in Canadian University Football Players
Bibliographic record
Abstract
Due to the physical nature of the game and repeated head impacts between players each play, the sport of football has one of the highest incidence rates of concussion. With nearly two million participants, this incidence rate translates to a reserved estimate of 100,000 concussions per year due to the contact nature of the sport. Injury thresholds have proven difficult to establish, so American football concussion research has shifted focus to measuring the accumulation of repetitive head impacts. As there are numerous rule differences between Canadian and American football, head impact exposure may present differently for Canadian players. Accordingly, the objective of this thesis was to investigate the effect of cumulative head impacts on Canadian university football players. This was achieved through three research projects using helmet-mounted sensors to monitor head impacts experienced by football players in practices and games, and measuring brain function via saccadic eye movements. Results illustrated that there were no differences in linear and rotational accelerations between striking and struck players during a collision. However, head impacts that occurred during kickoff plays experienced linear head accelerations that were double in magnitude and rotational head accelerations that were triple in magnitude than other special teams, offensive, and defensive plays (Chapter 2). Furthermore, the accumulation of head impacts significantly increased football players’ saccade latencies, which persisted over two successive seasons (Chapter 3). The total number of head impacts experienced during their career was significantly affected by a player’s position, and not their seniority (Chapter 4). In conclusion, this thesis identified football plays that resulted in high magnitude head accelerations, quantified the effect of individual head impacts on brain function using saccade latencies, and characterized career head impact exposure for football players. These results provide evidence that football head impact exposure needs to be reduced for the health of the players. Coaches and league administrators can use evidence-based research to employ strategies to reduce the number of head impacts to the sport of football.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".