9.7 Mental health outcomes for CFL athletes with ADHD and the impact of concussion
Bibliographic record
Abstract
Objective The purpose of the study is to evaluate mental health outcomes among Canadian Football League (CFL) athletes with (ADHD) and to measure outcomes based upon a history of concussion. Design Baseline neuropsychological evaluation at the beginning of the football season. Setting Professional Canadian Football League (CFL) athletes were recruited through their respective teams (n=9). Participants CFL athletes, n=796, all male, 443 (55.7%) Black, 265 (33.3%) White, 70 (8.8%) Other, 9 (1.1%) Hawaiian, 6 (0.8%) Indigenous, 3 (0.4%) Asian. Interventions (or Assessment of Risk Factors) Observational secondary study. Independent variable was the diagnosis of ADHD, Dependent variables were HRQL indexes of physical functioning, depression, and cognitive functioning. A second analysis compared ADHD athletes with no history of concussion to those who have at least one concussion across Health Related Quality of Life (HRQL) indexes. Outcome Measures Health Related Quality of Life. Main Results Participants diagnosed with ADHD (n=80) had statistically significant differences on all indices (Physical Functioning (t(782) = -3.359, p<.001), Depression ((t(782) = -2.820, p=.002), Cognitive (t(782)=-3.570, p<.001)), compared to athletes without ADHD. Among athletes with ADHD, no mental health differences were found between those who have at least one concussion and those who do not. Conclusions This study did not find a significant effect of concussion on mental health outcomes in participants with ADHD. However, athletes with ADHD present with higher mental health symptoms which may merit closer monitoring.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".