Adverse childhood experiences in relation to mood and anxiety disorders in a population-based sample of active military personnel
Bibliographic record
Abstract
BACKGROUND: Although it has been posited that exposure to adverse childhood experiences (ACEs) increases vulnerability to deployment stress, previous literature in this area has demonstrated conflicting results. Using a cross-sectional population-based sample of active military personnel, the present study examined the relationship between ACEs, deployment related stressors and mood and anxiety disorders. METHOD: Data were analyzed from the 2002 Canadian Community Health Survey-Canadian Forces Supplement (CCHS-CFS; n = 8340, age 18-54 years, response rate 81%). The following ACEs were self-reported retrospectively: childhood physical abuse, childhood sexual abuse, economic deprivation, exposure to domestic violence, parental divorce/separation, parental substance abuse problems, hospitalization as a child, and apprehension by a child protection service. DSM-IV mood and anxiety disorders [major depressive disorder, post-traumatic stress disorder (PTSD), generalized anxiety disorder (GAD), panic attacks/disorder and social phobia] were assessed using the composite international diagnostic interview (CIDI). RESULTS: Even after adjusting for the effects of deployment-related traumatic exposures (DRTEs), exposure to ACEs was significantly associated with past-year mood or anxiety disorder among men [adjusted odds ratio (aOR) 1.34, 99% confidence interval (CI) 1.03-1.73, p < 0.01] and women [aOR 1.37, 99% CI 1.00-1.89, p = 0.01]. Participants exposed to both ACEs and DRTEs had the highest prevalence of past-year mood or anxiety disorder in comparison to those who were exposed to either ACEs alone, DRTEs alone, or no exposure. CONCLUSIONS: ACEs are associated with several mood and anxiety disorders among active military personnel. Intervention strategies to prevent mental health problems should consider the utility of targeting soldiers with exposure to ACEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".