What is the association between traumatic life events and alcohol abuse/dependence in people with and without PTSD? Findings from a nationally representative sample
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".