Longitudinal analysis of adjustment to civilian life and self-rated mental health using the Life After Service Survey
Bibliographic record
Abstract
Introduction: Transitioning from the military can be a stressful time in a military member's life. Although evidence shows several factors are associated with a difficult transition, little is known about the long-term association of adjustment to civilian life and long-term mental health. Methods: Using the 2013 and 2019 longitudinal components of the Life After Service Survey, this study assessed the association between self-reported adjustment to civilian life in 2013 and self-reported mental health in 2019. Results: The logistic regression analysis showed that participants who reported a difficult transition in 2013 were 2.50 times more likely to report fair or poor mental health in 2019 (N = 2,015). In addition, participants who screened positive for posttraumatic stress disorder and those who reported fair or poor mental health in 2013 were 2.75 and 4.61 times more likely, respectively, to report fair or poor mental health in 2019. Discussion: The findings emphasize the importance of examining perceptions of the transition experience as a factor in long-term Veteran mental health.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".