Childhood maltreatment and aggressive behavior among college students: a serial mediation model of authenticity and alexithymia
Bibliographic record
Abstract
Introduction: Aggressive behavior among college students is a concerning issue that not only affects the mental health and personal development of those involved but also poses a threat to societal stability. Existing literature has consistently demonstrated a positive correlation between childhood maltreatment and aggressive behavior. However, the specific mechanisms through which childhood maltreatment leads to aggressive behavior remain unclear. This study aims to explore the impact of childhood maltreatment on aggressive behavior among college students and to examine the mediating roles of authenticity and alexithymia in this relationship. Methods: To investigate these relationships, we conducted an online survey among 1,148 Chinese college students. Participants completed the Childhood Trauma Questionnaire-Short Form (CTQ-SF), Authenticity Scale, Toronto Alexithymia Scale (TAS-20), and 12-item Aggression Questionnaire (12-AQ). These instruments allowed us to measure the variables of interest and to analyze the potential mediating effects of authenticity and alexithymia. Results: The findings of our study indicate that both authenticity and alexithymia mediate the positive relationship between childhood maltreatment and aggressive behavior. Specifically, the mediating effect of authenticity was 0.04 (95% CI [0.01, 0.06]), while that of alexithymia was 0.10 (95% CI [0.07, 0.13]). Moreover, we observed a chain-mediating effect involving both authenticity and alexithymia, with a chain-mediating effect of 0.03 (95% CI [0.02, 0.05]). Conclusions: This study demonstrates that childhood maltreatment can positively predict aggressive behavior in college students, and this relationship is mediated individually and sequentially by authenticity and alexithymia. Our findings contribute valuable insights to the existing research on aggressive behavior and provide a theoretical framework for developing interventions aimed at reducing aggressive behaviors among college students.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".