Network Analysis of Posttraumatic Stress and Eating Disorder Symptoms in a Community Sample of Adults Exposed to Childhood Abuse
Bibliographic record
Abstract
Posttraumatic stress disorder (PTSD) and eating disorders (EDs) are individually debilitating and highly comorbid conditions. Childhood abuse is a prominent risk factor for PTSD and ED symptoms both individually and as a comorbid syndrome (PTSD-ED). There may be a functional association between comorbid PTSD-ED symptoms whereby disordered eating behaviors are used to avoid trauma-related thoughts and feelings. The current study used a network analytic approach to examine key associations between PTSD and ED symptom subscales (i.e., PCL-5 and EPSI, respectively) in a community sample of 120 adults who endorsed at least one experience of childhood abuse (i.e., physical, sexual, or emotional abuse; witnessing domestic violence). Participants completed an anonymous online survey using Amazon's Mechanical Turk Prime. We used three network analysis indices (i.e., strength centrality, key players, and bridge symptoms) to identify symptoms that may maintain the comorbid PTSD-ED network. The results indicated that reexperiencing symptoms had the highest strength centrality in the PTSD-ED network and bridged the PTSD and ED clusters. For ED, cognitive restraint was a bridge to all PTSD symptoms. Hyperarousal, negative alterations in cognitions and mood (NACM), and purging were key players, indicating they are integral to the network structure. If replicated in prospective studies, these results may indicate that reexperiencing and cognitive restraint are core drivers of PTSD-ED comorbidity, whereas hyperarousal, NACM, and purging may be downstream consequences maintaining the comorbid condition. Concurrent treatments that address PTSD and ED symptoms simultaneously may result in the best outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".