Injuries in Canadian female high school rugby and coach perceptions of injury prevention: Informing an injury prevention implementation strategy
Bibliographic record
Abstract
This MSc thesis contains two projects focused on Canadian high school rugby. The first project is an evaluation of the epidemiology of female high school rugby in a Canadian context. Objective: To describe injury rates in female high school rugby and evaluate the association between baseline risk factors and injury outcomes. Methods: Injury surveillance was completed during a two-year prospective cohort study in the Calgary female high school rugby league. Results: Injury and concussion incidence rates were 93.7 injuries/ 1000 match-hours and 37.5 concussions/ 1000 match-hours, respectively. Injury within the past twelve months was associated with higher match injury rates. Higher team playing division was associated with higher training injury rates. The second project is an evaluation of the Canadian high school rugby coaching context. Objective: To describe the Canadian high school rugby coach context and evaluate intention to use a rugby-specific neuromuscular training warm-up. Methods: High school rugby coaches participated in a 2-hour “Train-the-Coach” neuromuscular training warm-up workshop. Pre- and post-workshop questionnaires were administered. Results: Pre-workshop, 92% of coaches agreed or strongly agreed they would ‘complete a rugby-specific warm-up program prior to every game and training session this season’. Post-workshop, 85% of coaches partly or strongly agreed that they “would conduct the SHRed Injuries program in every session with their students/athletes/client”. Conclusions: Injury and concussion rates in Canadian female high school rugby are high and intention to use a rugby-specific neuromuscular training warm-up was high before and after the workshop.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".