The impact of adverse childhood experiences (ACEs) and combat exposure on mental health conditions among new post-9/11 veterans.
Bibliographic record
Abstract
OBJECTIVE: Adverse childhood experiences (ACEs) are early life experiences of abuse and neglect, and observed violence, among others. For military veterans, both ACEs and combat exposure are associated with mental health problems. METHOD: This study examines the relationship between ACEs and combat exposure on the current mental health in a large sample of recent post-9/11 U.S. veterans. RESULTS: Fifty-nine percent of female and 39% of male veterans reported exposure to 1 ACE, whereas 44% of female and 25% of male veterans were exposed to multiple ACEs. Female veterans were more likely to experience 4 or more ACEs. ACEs were more consistently associated with mental health problems for male veterans than their female peers. For female veterans, exposure to 1 or 2 ACEs did not increase the odds of having any mental health condition, whereas for males, this level of exposure was associated with probable PTSD and anxiety. Combat patrol events were associated with an increase in the likelihood of having a probable mental health problem, with 2 exceptions-combat patrol events were not associated with depression in male veterans and not associated with alcohol misuse in female veterans. Combat was not associated with alcohol misuse. Experiencing a corollary of combat (e.g., accidents, moral injury) was inconsistently associated with the odds of having a probable mental health problem. CONCLUSIONS: This study confirms prior studies demonstrating a relationship between ACEs and combat on subsequent mental health problems. Importantly, 2 different types of combat exposure had differential effects on mental health problems. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".