Negotiating Concussion Risks by Canadian Amateur Rugby Athletes: A Grounded Theory
Bibliographic record
Abstract
Rugby union, a popular team contact and collision sport, carries a substantial risk of injury, including concussion. In response to growing concerns, interventions have been designed to manage concussion-related risks in rugby. However, these interventions have sometimes been met with greater risk-taking by athletes (e.g., hiding symptoms). This duality of risk tolerance and risk aversion highlights a tension within rugby and sport more broadly when attempts are made to negotiate personal risk thresholds. Despite the tensions, challenges, and differing perspectives on risk management in sports, theories on risk, safety, and concussion remain largely underdeveloped. Therefore, the purpose of this dissertation was to explore the process by which athletes negotiate thresholds of concussion risk in Canadian amateur rugby to develop a substantive theory. This study adopted a grounded theory methodology within a critical realist paradigm. In total, 30 members of the Canadian rugby community participated in semi-structured interviews (18 athletes, four coaches, three match officials, three administrators, and two medical support staff). Additionally, field observations were conducted during the 2022 and 2023 seasons. Participant interviews were transcribed and analysed alongside field notes following steps for Straussian grounded theory. A substantive theory and theoretical model were developed. The theory explains the micro- and macro-level structures and processes influencing athletes’ negotiations of concussion risk thresholds. The core category described how social structures carrying significant historical weight contributed to the central framing of concussion risks within the Canadian rugby community. This central framing created an environment whereby athletes are thrust into the process of negotiating their concussion risk thresholds as they attempt to navigate a complex injury experience within a competitive sporting environment. This work contributes to the existing body of knowledge on risk, sport, and concussion injury, offering rich, empirically grounded, and novel insights into the complex interrelation of factors influencing athletes as they engage in (re)negotiations of their concussion risk thresholds. Future research could adapt the theory across a more diverse sample of sport, injury, and risk experiences to further explore its explanatory power. Doing so may inform the development of the substantive theory in the current study into a formal theory.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.023 | 0.025 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".