Non-suicidal self-injury prevalence, course, and association with suicidal thoughts and behaviors in two large, representative samples of US Army soldiers
Bibliographic record
Abstract
BACKGROUND: Non-suicidal self-injury (NSSI) prospectively predicts suicidal thoughts and behaviors in civilian populations. Despite high rates of suicide among US military members, little is known about the prevalence and course of NSSI, or how NSSI relates to suicidal thoughts and behaviors, in military personnel. METHODS: We conducted secondary analyses of two representative surveys of active-duty soldiers (N = 21 449) and newly enlisted soldiers (N = 38 507) from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). RESULTS: The lifetime prevalence of NSSI is 6.3% (1.2% 12-month prevalence) in active-duty soldiers and 7.9% (1.3% 12-month prevalence) in new soldiers. Demographic risk factors for lifetime NSSI include female sex, younger age, non-Hispanic white ethnicity, never having married, and lower educational attainment. The association of NSSI with temporally primary internalizing and externalizing disorders varies by service history (new v. active-duty soldiers) and gender (men v. women). In both active-duty and new soldiers, NSSI is associated with increased odds of subsequent onset of suicidal ideation [adjusted odds ratio (OR) = 1.66-1.81] and suicide attempts (adjusted OR = 2.02-2.43), although not with the transition from ideation to attempt (adjusted OR = 0.92-1.36). Soldiers with a history of NSSI are more likely to have made multiple suicide attempts, compared with soldiers without NSSI. CONCLUSIONS: NSSI is prevalent among US Army soldiers and is associated with significantly increased odds of later suicidal thoughts and behaviors, even after NSSI has resolved. Suicide risk assessments in military populations should screen for history of NSSI.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".