What is the association between traumatic life events and alcohol abuse/dependence in people with and without PTSD? Findings from a nationally representative sample
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Approximately 60-90% of the general population will experience a traumatic event during their lifetime. However, relatively few will develop a trauma-related psychological disorder. Possible psychological sequelae of trauma include posttraumatic stress disorder (PTSD) and alcohol-use disorders (AUDs). While AUDs often occur in the context of PTSD, little is known about the degree to which AUDs are attributable to specific traumatic events. The purpose of the present investigation was to assess the degree to which specific traumatic events are predictive of AUDs in people with and without PTSD. METHODS: The current sample was selected from the National Epidemiological Survey of Alcohol and Related Conditions (NESARC; N = 34,160), a nationally representative sample of American adults. Multiple logistic regressions were performed to examine odds ratios of 27 traumatic events among individuals with and without PTSD in the prediction of AUD diagnoses. RESULTS: Results indicated significant positive odds ratios among individuals meeting criteria for PTSD and having experienced a childhood trauma (OR = 1.40 [95% CI: 1.08-1.83], P<.01) or assaultive violence (OR = 1.41 [95% CI: 1.13-1.77], P<.01) for predicting AUDs. Also, among individuals without PTSD, childhood trauma (OR = 1.32 [95% CI: 1.23-1.41], P<.001), assaultive violence (OR = 1.42 [95% CI: 1.13-1.78], P<.001), unexpected death (OR = 1.19 [95% CI: 1.12-1.28], P<.001), and learning of trauma (OR = 1.22 [95% CI: 1.13-1.30], P<.001) positively predicted the presence of AUDs. CONCLUSIONS: Results indicate significant positive relationships between traumatic events and AUDs, particularly among individuals without PTSD. Specific associations and theoretical implications will be discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it