A grounded theory of youth athlete concussion under-reporting decisions
Bibliographic record
Abstract
BACKGROUND: Sport-related concussions leave youth vulnerable to significant health, physical, social, and educational disruptions. Many youth athletes under-report concussions, but the scope of the issue for this population is not well understood, nor are the underlying motivations for (under)reporting. It is necessary to examine the relationship between sport-related concussion knowledge and reporting and invite athletes to share their decision-making processes. The purpose of the research was to understand how athletes' attitudes, experiences and beliefs regarding concussions influence their under-reporting decisions and behaviours. METHODS: We conducted semi-structured interviews with 17 elite athletes, aged 16-18 years, from individual and team sports. Data were analyzed using constructivist grounded theory and a substantive theoretical model of sport-related concussion under-reporting was developed. RESULTS: The model demonstrates personal, social, cultural, and biophysical processes that contribute to under-reporting decisions and identifies multiple interconnected processes that contribute to 'high-risk' situations of under-reporting. Youth athletes navigate various barriers and facilitators to under-reporting, including their own desire to play, perceived stigma, concussion sense-making, and social influences. Results suggest that under-reporting by youth athletes implicates a collective, rather than individual, responsibility. CONCLUSIONS: A theoretical grounded model for youth sport-related concussion under-reporting highlights the complexity of the decision-making process. This study offers a new framework for understanding why youth athletes may hide concussion symptoms. The model offers potential to be used to develop concussion interventions and tools for improved decision-making, and to address issues of risk and responsibility concerning concussion reporting.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| 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".