A Network Analysis of Embodied Trauma: Self-Destructive Behaviors Among Survivors of Childhood and Adolescent/Adult Sexual Assault
Bibliographic record
Abstract
This study capitalized on a network analysis to examine the interrelations between childhood sexual abuse (CSA), adolescent/adult sexual assault (AASA), and self-destructive behaviors (i.e., binge eating, self-harm, and substance use) within a network framework, guided by the principles of embodiment theory. It further aimed to explore how these patterns vary across gender identities by estimating and comparing three distinct networks for men, women, and gender-diverse individuals. Data were drawn from the International Sex Survey (N = 82,243; M_age = 32.39, SD = 12.52) conducted across 42 countries. Network analyses were conducted separately for men, women, and gender-diverse individuals using EBIC Graphical Lasso. Differences in network structure, edge weights, and centrality metrics were tested using network comparison tests, with false-discovery-rate corrections applied. Among women, CSA emerged as the most central node in the network, showing strong connections to AASA, binge eating, self-harm, and substance use, suggesting an integrated pattern of embodied coping. In contrast, the network for gender-diverse individuals revealed two partially distinct sub-networks: one linking CSA, AASA, and substance use, and another connecting binge eating and self-harm – suggesting different coping mechanisms. The network estimated for men was more fragmented, with weaker and fewer connections among trauma and measures of self-destructive behaviors. Findings suggest the importance of gender-sensitive, trauma-informed care. CSA emerged as a central driver of interconnected self-destructive behaviors in women, while gender-diverse individuals showed distinct patterns, suggesting embodiment and minority stress as potential risk factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".