Physiological underarousal as a mechanism of aggressive behavior in university athletes with a history of concussion
Bibliographic record
Abstract
INTRODUCTION: Research has indicated that athletes who engage in high-risk athletic activities, such as football and hockey, have riskier personalities than their low-risk and nonathlete counterparts (Ahmadi et al., 2011, Procedia Soc Behav Sci, 30 and 247-251; Zuckerman, 1983, Biological bases of sensation seeking, impulsivity, and anxiety, Lawrence Erlbaum Assoc Inc.). For instance, increased sensation-seeking and aggression are common in high-risk athletes, rendering these individuals more likely to sustain a subsequent injury, such as concussion. Elevated levels of certain personality traits, including impulsivity and aggression, have also been observed after concussion (Goswami et al., 2016, Brain Struct Funct, 221 and 1911-1925). The purpose of this study therefore was to determine whether aggressive behavior in university athletes may be accounted for, in part, by a history of concussion, rather than exclusively athletic status. METHODS: Using a quasi-experimental design, 66 university students (n = 18 nonathletes, n = 24 low-risk athletes, n = 24 high-risk athletes) with (n = 27) and without a history of concussion (n = 39) completed the Buss & Perry Aggression Questionnaire (BPAQ; Buss & Perry, , J Pers Soc Psychol, 63 and 452) and provided electrodermal activation (EDA) as an index of physiological arousal. RESULTS: It was found that decreased physiological arousal among students with a history of concussion was associated with greater endorsement of physical aggression. Moreover, athletic status did not account for this pattern of aggression, as athletes and nonathletes did not differ in terms of self-reported aggressive tendencies. CONCLUSIONS: Physiological compromise after concussive injury may act as an independent mechanism of aggressive behavior in athletes beyond factors, such as athletic status.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".