Does rugby headgear prevent concussion? Attitudes of Canadian players and coaches
Bibliographic record
Abstract
OBJECTIVES: To examine the attitudes of players and coaches to the use of protective headgear, particularly with respect to the prevention of concussion. METHODS: A questionnaire designed to assess attitudes to headgear was administered to 63 players from four different Canadian teams, each representing a different level of play (high school, university, community club, national). In addition, coaches from all four levels were questioned about team policies and their personal opinions about the use of headgear to prevent concussion. RESULTS: Although the players tended to believe that the headgear could prevent concussion (62%), the coaches were less convinced (33%). Despite the players' belief that headgear offers protection against concussion, only a minority reported wearing headgear (27%) and few (24%) felt that its use should be made mandatory. Common reasons for not wearing headgear were "its use is not mandatory", "it is uncomfortable", and "it costs too much". CONCLUSION: Although most players in the study believe that rugby headgear may prevent concussion, only a minority reported wearing it. Coaches tended to be less convinced than the players that rugby headgear can prevent concussion.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".