An examination of social relations and concussion management via the blue card
Bibliographic record
Abstract
Introduction: Initially developed by New Zealand Rugby in 2014, the Blue Card initiative in rugby enables match officials to remove athletes from play if they are suspected to have sustained a concussion. Considerable attention has been paid by sport and health advocates to the possibilities and limitations of this initiative in safeguarding athlete health. However, little if any attention has been paid to the well-being of those responsible for administering the Blue Card (i.e., match officials). The aim of this paper was to examine match officials' experiences with and perspectives on implementing the Blue Card initiative in Ontario, Canada, with focused attention on the tensions around their ability to manage games and participants (e.g., athletes, coaches) while attempting to safeguard athlete well-being. Methods: Using Relational Coordination Theory (RCT) as a guiding framework and qualitative research method, we highlight the rich accounts of 19 match officials' perspectives and experiences regarding sport-related concussion (SRC) management and the Blue Card protocol. Results: . Discussion: Our findings emphasize the need to attend to social relations in concussion management and provide insight into match officials' fraught experiences on the frontlines of concussion management. We identify factors affecting match official well-being and provide considerations for concussion management initiatives designed to improve athlete safety, such as the Blue Card.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".