A Theoretical and Empirical Linkage between Road Accidents and Binge Eating Behaviors in Adolescence
Bibliographic record
Abstract
This study aimed at identifying specific clusters of maladaptive emotional–behavioral symptoms in adolescent victims of motorbike collisions considering their scores on alexithymia and impulsivity in addition to examining the prevalence of clinical binge eating behaviors (respectively through the Youth Self-Report (YSR), Toronto Alexithymia Scale-20 (TAS-20), Barratt Impulsiveness Scale-11 (BIS-11), and Binge Eating Scale (BES)). Emotional–behavioral profiles, difficulties in identifying and describing feelings, impulsivity, and binge eating behaviors have been assessed in 159 adolescents addressing emergency departments following motorbike collisions. Our results showed a cluster of adolescents with clinical binge eating behaviors, high rates of motorbike accidents, and high levels of internalizing and externalizing problems, alexithymia, and impulsivity (23.3% of the sample); a second cluster of adolescents with clinical binge eating behaviors, a moderate number of collisions, and moderate levels of emotional and behavioral problems on the above four dimensions (25.8% of the sample); and a third cluster of youth without clinical binge eating behaviors, with a moderate number of accidents, and with low scores on the four dimensions (50.9% of the sample). Adolescents of Cluster 1 showed a higher likelihood to be involved in motorbike collisions than the youth in Clusters 2 and 3 (p < 0.0001). We suggest that adolescents’ motor collisions could be associated with their difficulties in emotion regulation and with their impaired psychological profiles, which could also underpin their disordered eating. The identification of specific clusters of psychopathological symptoms among this population could be useful for the construction of prevention and intervention programs aimed at reducing motor collision recidivism and alleviating co-occurring psychopathologies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".