Mental Health and Comorbidities in U.S. Military Members
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Using data from a prospective cohort study of U.S. service members who joined after September 11, 2001 to determine incidence rates and comorbidities of mental and behavioral disorders. METHODS: Calculated age and sex adjusted incidence rates of mental and behavioral conditions determined by validated instruments and electronic medical records. RESULTS: Of 10,671 service members, 3,379 (32%) deployed between baseline and follow-up, of whom 49% reported combat experience. Combat deployers had highest incidence rates of post-traumatic stress disorder (PTSD) (25 cases/1,000 person-years [PY]), panic/anxiety (21/1,000 PY), and any mental disorder (34/1,000 PY). Nondeployers had substantial rates of mental conditions (11, 13, and 18 cases/1,000 PY). Among combat deployers, 12% screened positive for mental disorder, 59% binge drinking, 16% alcohol problem, 19% cigarette smoking, and 20% smokeless tobacco at follow-up. Of those with recent PTSD, 73% concurrently developed >1 incident mental or behavioral conditions. Of those screening positive for PTSD, 11% had electronic medical record diagnosis. CONCLUSIONS: U.S. service members joining during recent conflicts experienced high rates of mental and behavioral disorders. Highest rates were among combat deployers. Most cases were not represented in medical codes, suggesting targeted interventions are needed to address the burden of mental disorders among service members and Veterans.
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.
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) | 0.000 | 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) | 0.005 | 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 it