Coping strategies used by varsity athletes with a concussion
Bibliographic record
Abstract
More than 85% of concussions in individuals between the ages of 16-34 are sports-related (Gordon, Dooley, & Wood, 2006). Concussions are associated with somatic, cognitive, or emotional symptoms and negatively affect the health and quality of life of injured athletes (McCrory et al., 2013). The use of effective coping strategies has been investigated in non-concussed injured athletes (Johnston & Carroll, 2000; Udry, Gould, Bridges, & Beck, 1997) and individuals with concussions (Borg, Holm, Peloso, & Cassidy, 2004; Azulay, Smart, Mott, & Cicierone, 2013). However, little research has investigated the coping strategies used by athletes following concussions. Therefore, this study aims to understand varsity athletes’ experiences of sustaining a concussion and how varsity athletes cope with the effects of concussions. We conducted semi-structured interviews (ranging from 45-70 min, M = 55 min.) with five university varsity athletes (2 male, 3 female, M age = 21) from team and individual sports who sustained a sport-related concussion. Data were analyzed using Interpretative Phenomenological Analysis (Smith, Flowers & Larkin, 2009). Results indicated that after their concussion participants felt removed from all aspects of their life: academics, sport and social. They experienced emotional distress during their gradual return to school and activity. Athletes used social support, emotion focused coping strategies and problem focused coping strategies to facilitate their recovery. Athletes discussed advice they would provide to a fellow varsity athlete with a concussion, including being honest with yourself and your medical staff, and surrounding yourself with a strong social support system. This research addresses some of the challenges associated with following a step-wise return to sport/school protocol and some of the coping strategies that may facilitate varsity athletes’ rehabilitation process. Keywords: concussed athlete, recovery, social support, phenomenological, qualitative
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".