Screening for Anxiety and Depression Symptoms Using Concussion Symptom Scales Among Varsity Athletes
Bibliographic record
Abstract
OBJECTIVE: This study examined associations between Sport Concussion Assessment Tool-5 (SCAT-5) symptom reporting and gold-standard measures of anxiety and depression, and explored the utility SCAT-5 symptom subscales to identify anxiety and depression symptomology. DESIGN: Prospective cross-sectional study. SETTING: York University in Toronto, Canada. PARTICIPANTS: Preseason data were collected for varsity athletes (N = 296) aged between 17 and 25 years ( M = 20.01 years, SD = 1.69 years; 52% male). MAIN OUTCOME MEASURES: The SCAT-5 symptom evaluation scale was used to assess baseline symptoms. The Generalized Anxiety Disorder Index-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) were used to assess symptoms of anxiety and depression, respectively. RESULTS: Endorsement of SCAT-5 symptoms of feeling anxious, sadness, irritability, and feeling more emotional had the strongest correlations with the GAD-7 ( r' s > 0.400; P' s < 0.001). Sadness, trouble falling asleep, concentration problems, feeling slowed down, anxious, irritability, mental fog, fatigue, and memory problems had the highest correlations with the PHQ-9 ( r' s >0 .400; P' s < 0.001). The Emotional subscale from the SCAT-5 predicted mild to severe anxiety on the GAD-7 ( P < 0.001). The Sleep, Cognitive, and Emotional subscales predicted mild to severe depression on the PHQ-9 ( P' s < 0.05). CONCLUSIONS: These findings provide better delineation of symptoms endorsed on the SCAT-5 symptoms that aid in identification of athletes with symptoms of anxiety or depression who may be at risk for developing a clinical disorder or experiencing persistent symptoms after a 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".