A-49 Sideline Concussion Assessment Tool symptoms predict poorer mental health outcomes in college athletes
Bibliographic record
Abstract
Abstract Purpose: Assessment of emotional functioning is essential in sports-related concussion (SRC) management. This study investigated the prediction of mental health outcomes from the SCAT-3 Symptom Scale. Methods: Canadian university athletes who compete in contact sports participated in a large-scale study (Active Rehabilitation). A total of 235 participants (males = 67%) completed the Brief Symptom Inventory (BSI), Quality of Life Scale (QOLS), and Sideline Concussion Assessment Tool, 3rd edition (SCAT). Step-wise regression analysis using demographic variables (gender, age, history of Learning Disability, history of ADHD, diagnosis of psychiatric condition) and psychological symptoms from the SCAT were used as predictors for the BSI (Depression and Anxiety) and QOLS (Depression and Anxiety). Results: Regression analysis revealed female gender, previous psychiatric diagnosis and Nervousness (SCAT) significantly predicted higher scores on the Depression subscales of BSI (R2 = 0.21, F1,231 = 20.3, p < 0.001) and Quality of Life (R2 = 0.21, F1,231 = 18.0, p < 0.001). Female gender, previous psychiatric diagnosis, Nervousness and Irritability predicted higher scores on both the Anxiety subscales of the BSI (R2 = 0.23, F1,230 = 16.9, p < 0.001) and Quality of Life (R2 = 0.26, F1,230 = 20.2, p < 0.001). Contrary to other findings, history of concussion was not significantly predictive of higher Depression and/or Anxiety scores (although there was a trend between number of concussions and higher symptom report). Conclusions: SCAT symptoms may capture heightened emotional symptom report with more robust scales measuring emotional functioning. In contrast, concussion history did not predict emotional symptoms, a finding contrary to published literature.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".