A-12 Mental Health Outcomes for CFL Athletes with ADHD
Bibliographic record
Abstract
Abstract Purpose: Current research suggests that athletes with Attention Deficit Hyperactivity Disorder (ADHD) are at risk for poorer outcomes arising from sport related concussions (SRC). There is limited research related evaluating mental health outcomes in athletes diagnosed with ADHD. 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. Methods: Professional football players from the CFL (n = 784) were administered a baseline mental health measure, the Health-Related Quality of Life (HRQL), as a component of pre-season evaluations as part of a larger cohort study (Active Rehabilitation). Diagnosis of ADHD was the independent variable and HRQL index scores measuring physical functioning, depression, cognitive functioning, were dependent variables. A second analysis compared ADHD athletes with no history of concussion to those with a history of concussion on the HRQL indexes. Results: Participants diagnosed with ADHD (n = 80) had statistically significant differences on the Physical Functioning (t(782) = −3.359, p < 0.001), Depression ((t(782) = −2.820, p = 0.002) and Cognitive (t(782) = −3.570, p < 0.001) domains of the HRQL, compared to athletes without ADHD. Among the athletes who are diagnosed 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".