Projected Rates of Psychological Disorders and Suicidality Among Soldiers Based on Simulations of Matched General Population Data
Bibliographic record
Abstract
Limited data are available on lifetime prevalence and age-of-onset distributions of psychological disorders and suicidal behaviors among Army personnel. We used simulation methods to approximate such estimates based on analysis of data from a U.S. national general population survey with the sociodemographic profile of U.S. Army personnel. Estimated lifetime prevalence of any Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) anxiety, mood, behavior, or substance disorder in this sample was 53.1% (17.7% for mood disorders, 27.2% for anxiety disorders, 22.7% for behavior disorders, and 14.4% for substance disorders). The vast majority of cases had onsets before the expected age of enlistment if they were in the Army (91.6%). Lifetime prevalence was 14.2% for suicidal ideation, 5.4% for suicide plans, and 4.5% for suicide attempts. The proportion of estimated preenlistment onsets was between 68.4% (suicide plans) and 82.4% (suicidal ideation). Externalizing disorders with onsets before expected age of enlistment and internalizing disorders with onsets after expected age of enlistment significantly predicted postenlistment suicide attempts, with population attributable risk proportions of 41.8% and 38.8%, respectively. Implications of these findings are discussed for interventions designed to screen, detect, and treat psychological disorders and suicidality in the Army.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".