Trauma exposure, DSM-5 posttraumatic stress disorder, and binge eating: Results from a nationally representative sample
Bibliographic record
Abstract
Important links between trauma exposure, post-traumatic stress disorder (PTSD), and engagement in health risk behaviors have been demonstrated. However, most previous studies have utilized self-report measures rather than diagnostic interviews in assessing post-traumatic stress symptoms, have assessed PTSD using DSM-IV rather than contemporary DSM-5 diagnostic criteria, and have focused their investigations on select maladaptive health behaviors (e.g., substance use, smoking) and/or populations (e.g., clinical samples, women). Importantly, possible gender differences in the occurrence of these associations have been neglected. The current study will execute three aims in a nationally representative sample of the US general population: 1) Examine the impact of trauma exposure, PTSD, and subthreshold PTSD on a number of health risk behaviors (i.e., overeating, risky sexual behavior, smoking, and lack of physical activity), irrespective of the presence of comorbid mental disorders. 2) Investigate whether these associations differ in men and women. 3) Clarify whether the specific nature of the trauma (e.g., child abuse, motor vehicle accident, natural disaster) and PTSD symptom clusters display differential links with health risk behaviors. Participants and Methodology: Data will come from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III: 2012-2013), a nationally representative sample of 36,309 non-institutionalized adults residing in the US. PTSD and other mental disorders were assessed with a computer-assisted, structured diagnostic interview. All interviews were conducted face-to-face. Implications: The results of this study could guide the development of population-based approaches for targeting health behavior change within trauma-exposed populations, and inform gender-specific pathways between posttraumatic symptoms and specific physical health conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".