Mediating effects of impulsivity and alexithymia in the association between traumatic brain injury and aggression in incarcerated males
Bibliographic record
Abstract
Studies suggest both alexithymia and impulsivity (partially) explain aggressive behavior in traumatic brain injury (TBI) patients, but none of these studies use both questionnaire and performance-based measures as recommended, nor simultaneously investigate both impulsivity and alexithymia. The available studies therefore likely miss part of the constructs of alexithymia and impulsivity, and do not comprehensively assess the mediating effects of both constructs in the relationship between TBI and aggression. A sample of N = 281 incarcerated individuals were recruited from Dutch penitentiary institutions, and completed the Buss Perry Aggression Questionnaire (aggression), BIS-11 (impulsivity) and Toronto Alexithymia Scale-20 (alexithymia) questionnaires, as well as a stop-signal task and an emotion recognition paradigm. Several multiple mediation analyses were conducted using structural equation modelling, to assess the viability of a causal theoretical model of aggression. The final planned models were the original models with a good fit with the data (comparative fit index > 0.95, root mean square error of approximation and Standardized root mean square residual < 0.05), and results indicate that only questionnaire-based impulsivity mediated the relationship between TBI and aggression. TBI was unrelated to alexithymia, stop-signal or emotion recognition performance. Aggression was predicted by both alexithymia and impulsivity, but not by the performance measures. Post hoc analyses shows that alexithymia moderates the relationship between impulsivity and aggression. These results imply that aggressive incarcerated individuals showing impulsive behavior should be screened for TBI, since TBI is often overlooked or misdiagnosed, and indicate that both impulsivity and alexithymia are potential focus points for aggression reduction treatment in TBI patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".