9.17 Scat symptom reporting and mental health screening in collegiate athletes
Bibliographic record
Abstract
Objective To evaluate the relationship between subjective mood symptoms on the SCAT and more comprehensive mental health screening measures during baseline testing. Design Retrospective cross-sectional design. Setting Baseline concussion evaluations at a Canadian university. Participants 248 Consented participants (n = 164 males and 86 females) who underwent baseline screening evaluations. Interventions (or Assessment of Risk Factors) Athletes completed the Sport Concussion Assessment Tool (SCAT3), the Brief Symptom Inventory (BSI-18) and Patient-Reported Outcomes Measurement Information System (PROMIS-29) to determine how well SCAT3 mood symptoms predicted broader measures of depression and anxiety. Outcome Measures correlation and regression analysis. Main Results Of the 4 individually rated SCAT3 symptoms of mood, ‘sadness’ was most strongly correlated with the depression indexes of the BSI-18 [r(246) = 0.47, p < 0.01)] and PROMIS-29 [r(246) = 0.46, p < 0.01)]. Regression analyses suggests sadness best explained the greatest variance in the depression index scores from the BSI-18 [F(4, 238) = 20.6, p < .01, R2 = .26, R2Adjusted = .25) and PROMIS-29 [F(4, 238) = 31.6, p < .01, R2 = .35, R2Adjusted = .34). Similar significant findings were noted for the symptom of ‘nervousness’ on the anxiety index scores of the BSI-18 and PROMIS-29. Conclusions Subjective symptom on the SCAT 3, specifically ‘sadness’ and ‘nervousness’ appear to reasonably predict more comprehensive ratings of depression and anxiety. This information may help clinicians identify athletes who may be dealing with mental health issues when more comprehensive questionnaires are not available.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".