Athlete Fear Avoidance, Depression, and Anxiety Are Associated with Acute Concussion Symptoms in Athletes
Bibliographic record
Abstract
Background: Assessing sport-related concussions in athletes presents challenges due to symptom variability. This study aimed to explore the relationship between acute concussion symptoms and athlete fear avoidance, pain catastrophizing, depression, and anxiety. Anxiety and depression have previously been associated with the number of symptoms after a concussion, but no prior research has examined the possible link between athlete fear avoidance and acute concussion symptoms. Methods: Thirty-four collegiate athletes (mean age = 20.9 ± 1.8 years) were assessed within 48 h of a concussion using the Sport Concussion Assessment Tool 5, Athlete Fear Avoidance Questionnaire (AFAQ), Pain Catastrophizing Scale, and Hospital Anxiety and Depression Scale. Results: Results showed a significant association between the athlete fear avoidance and the number of concussion symptoms (r = 0.493, p = 0.003), as well as depression and anxiety measured by HADS (r = 0.686, p < 0.001). Athlete fear avoidance and HADS scores were predictors of symptom severity, explaining 41% of the variance (p = 0.001). Athletes with higher fear avoidance tended to report more symptoms post concussion. Conclusions: This study underscores the link between athlete fear avoidance, anxiety, depression, and the severity of concussion symptoms. Administering the AFAQ to assess athlete fear avoidance at the initial assessment of a concussion may be helpful in interpreting the symptoms of an acute concussion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".