Deployment and the Use of Mental Health Services among U.S. Army Wives
Bibliographic record
Abstract
BACKGROUND: Military operations in Iraq and Afghanistan have involved the frequent and extended deployment of military personnel, many of whom are married. The effect of deployment on mental health in military spouses is largely unstudied. METHODS: We examined electronic medical-record data for outpatient care received between 2003 and 2006 by 250,626 wives of active-duty U.S. Army soldiers. After adjustment for the sociodemographic characteristics and the mental health history of the wives, as well as the number of deployments of the personnel, we compared mental health diagnoses according to the number of months of deployment in Operation Iraqi Freedom in the Iraq-Kuwait region and Operation Enduring Freedom in Afghanistan during the same period. RESULTS: The deployment of spouses and the length of deployment were associated with mental health diagnoses. In adjusted analyses, as compared with wives of personnel who were not deployed, women whose husbands were deployed for 1 to 11 months received more diagnoses of depressive disorders (27.4 excess cases per 1000 women; 95% confidence interval [CI], 22.4 to 32.3), sleep disorders (11.6 excess cases per 1000; 95% CI, 8.3 to 14.8), anxiety (15.7 excess cases per 1000; 95% CI, 11.8 to 19.6), and acute stress reaction and adjustment disorders (12.0 excess cases per 1000; 95% CI, 8.6 to 15.4). Deployment for more than 11 months was associated with 39.3 excess cases of depressive disorders (95% CI, 33.2 to 45.4), 23.5 excess cases of sleep disorders (95% CI, 19.4 to 27.6), 18.7 excess cases of anxiety (95% CI, 13.9 to 23.5), and 16.4 excess cases of acute stress reaction and adjustment disorders (95% CI, 12.2 to 20.6). CONCLUSIONS: Prolonged deployment was associated with more mental health diagnoses among U.S. Army wives, and these findings may have relevance for prevention and treatment efforts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".