Hard-Headed Decisions: Intrapersonal Factors Underlying Concussion Reporting in University Athletes
Bibliographic record
Abstract
Most of the research investigating sports concussion (SC) disclosure has been conducted using questionnaires with a pre-determined set of questions. Hence, significant gaps remain in our understanding of which factors weight in the decision-making process underlying SC disclosure and how they contribute to it. This present study aims to fill some of these gaps using qualitative methods to identify intrapersonal determinants of SC disclosure and describe their influence on an athlete's decision-making process. Our results are based on in-depth, semistructured interviews (range, 56-79 min; total = 587 min) with 9 university athletes (5 females, 4 males) from three team sports (soccer, rugby, and cheerleading). Using constant comparative analysis guided by Straussian grounded theory, we identified 13 concepts, across three major intrapersonal categories (i.e., attitudes and behaviors; concussion knowledge; and subjective evaluation of the concussion), contributing to SC disclosure, including novel determinants such as prioritization of athletic versus intellectual activities and maturity level. Our results suggest that a comparison between experiential knowledge and severity of the injury plays a major role in determining an athlete's disclosure behaviors. Athletes with a history of concussion seem to adopt a non-disclosure default strategy and are inclined to disclose their concussion symptoms only if they judge their current concussion to be worse than their previous most severe injury. Other concepts identified appear to contribute to the decisional process by modulating the adoption of this non-disclosure default strategy. Our work highlights the benefits and necessity of using qualitative methods to study the decision-making process underlying concussion disclosure.
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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 | 0.038 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".