A - 25 Screening for Anxiety and Depression Symptoms using the Post-Concussive Symptom Scale Among Varsity Athletes
Bibliographic record
Abstract
Abstract Objective Symptoms of anxiety and depression are prevalent among athletes and often overlap with symptoms of a concussion. Clinical screeners of anxiety and depression are infrequently used by athletic teams despite the relationship between affective symptoms and protracted post-concussion recovery. The study objective was to examine associations between individual symptoms on the post-concussive symptom scale (PCSS) and gold standard measures of anxiety and depression. Methods Pre-season baseline data was collected for 296 varsity athletes from York University, Toronto. Participants were between the ages of 17 and 25 (M = 20.01 yrs, SD = 1.69 yrs; 52% male). The PCSS from the SCAT-5 was used to assess baseline symptoms. Generali. Results Moderate to strong correlations were noted between specific items of the PCSS and the GAD-7 and PHQ-9. Feeling anxious (r = 0.55), concentration problems (r = 0.40), irritability (r = 0.39), trouble falling asleep (r = 0.38), fatigue (r = 0.36), and mental fog (r = 0.35) were the highest correlations with the GAD-7 (ps < 0.001). Trouble falling asleep (r = 0.46), fatigue (r = 0.44), concentration problems (r = 0.41), memory problems (r = 0.37), feeling slowed down (r = 0.36), anxious (r = 0.36), and irritability (r = 0.36) were the highest correlations with the PHQ-9 (ps < 0.001). Conclusions These findings allow for better delineation of symptoms of the PCSS that aid in identification of athletes with symptoms of anxiety or depression, who may be at risk for endorsing persistent symptoms following 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".