2.10 How does mental health affect cognitive performance at baseline?
Bibliographic record
Abstract
Objective The purpose of this study is to understand the relationship between mental health issues and cognitive performance of Canadian Football League (CFL) athletes at baseline. Design This study data was used as a prospective, quasi-experimental design. The participants were naturally collected with no random selection. Setting CFL football stadium. Participants 784 Canadian football league (CFL) athletes underwent cognitive testing (ImPACT, BSI-18 and PROMIS 29) at baseline. Predictor Measures The patient-reported outcome measurement information system (PROMIS 29 and the BSI-18) will be used and the index scores of anxiety, depression, and somatic domains will be used. Outcome Measures Immediate Post-concussion assessment and cognitive testing (ImPACT). The five domains analyzed include verbal memory, visual memory, visual-motor processing speed, reaction time, and impulse control. History of concussions was added in as a covariate. Main Results Total somatic symptoms scores and impulse control composite score were positively correlated (r= .077) while anxiety total and visual-motor speed were positively related (r=.085). The history of concussions did not increase the variance that was explained by the models Conclusions This shows that athletes with somatic symptoms had worse impulse control and had difficulty controlling their impulse reactions compared to athletes without somatic issues. Additionally, athletes with anxiety had increased visual-motor speed, which is consistent with some research that showed that anxious individuals tend to work at a faster speed than their non-anxious counterparts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".