Trajectories of trauma symptoms and resilience in deployed US military service members: Prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Most previous attempts to determine the psychological cost of military deployment have been limited by reliance on convenience samples, lack of pre-deployment data or confidentiality and cross-sectional designs. AIMS: This study addressed these limitations using a population-based, prospective cohort of U.S. military personnel deployed in support of the operations in Iraq and Afghanistan. METHOD: The sample consisted of U.S. military service members in all branches including active duty, reserve and national guard who deployed once (n = 3393) or multiple times (n = 4394). Self-reported symptoms of post-traumatic stress were obtained prior to deployment and at two follow-ups spaced 3 years apart. Data were examined for longitudinal trajectories using latent growth mixture modelling. RESULTS: Each analysis revealed remarkably similar post-traumatic stress trajectories across time. The most common pattern was low-stable post-traumatic stress or resilience (83.1% single deployers, 84.9% multiple deployers), moderate-improving (8.0%, 8.5%), then worsening-chronic post-traumatic stress (6.7%, 4.5%), high-stable (2.2% single deployers only) and high-improving (2.2% multiple deployers only). Covariates associated with each trajectory were identified. CONCLUSIONS: The final models exhibited similar types of trajectories for single and multiple deployers; most notably, the stable trajectory of low post-traumatic stress preto post-deployment, or resilience, was exceptionally high. Several factors predicting trajectories were identified, which we hope will assist in future research aimed at decreasing the risk of post-traumatic stress disorder among deployers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".