Military Veterans’ Psychological Health and Physical Activity After Separation From Service
Bibliographic record
Abstract
INTRODUCTION: The transition from military to civilian life can bring about substantive challenges for U.S. veterans. The purpose of this study was to examine veterans' trajectories of psychological health prior to and after separation, and to examine whether veterans who engaged in more physical activity would report better psychological health over time. METHODS: =36.3 years, SD=10.9 at baseline) who had self-report data collected prior to and on at least 2 time points after separation were analyzed. Psychological health was operationalized via measures of self-reported mental health-related quality of life, depressive symptoms, and post-traumatic stress disorder symptoms. Physical activity was measured using self-reported minutes per week of moderate-to-vigorous physical activity. Parallel process latent growth modeling was used to examine the relationship between physical activity and psychological health. RESULTS: Results revealed decreases in psychological health after separation. Veterans with higher pre-separation physical activity were more likely to display steeper trajectories of decreased physical activity and psychological health after separation. In contrast, veterans who engaged in higher levels of physical activity after separation displayed increases in psychological health after separation. CONCLUSIONS: Findings suggest that high levels of physical activity during service may not protect against worsened psychological health trajectories after separation. However, the results provide support for the potential protective factor of physical activity after separation on psychological health symptoms after separation.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".