Psychosocial factors and military-to-civilian transition challenges: A dyadic analysis of Veterans and their spouses
Bibliographic record
Abstract
Introduction: Limited research has focused on the military-to-civilian transition from the perspective of both Veterans and their spouses and on the role each may play in shaping one another's experiences during this time. This study is a dyadic analysis of psychosocial factors associated with the experience of challenges during the military-to-civilian transition among Canadian Armed Forces (CAF) Veterans and their spouses. Methods: Analyses were conducted on data from the CAF Transition and Well-being Survey, which assessed well-being among Veterans who recently transitioned out of the CAF and, if applicable, their spouses. Structural equation analyses were performed on couple dyads to investigate the associations of Veterans' and spouses' psychosocial attributes with their perceived transition challenges and those of their partners. Results: Veterans' social support and sense of community belonging were associated with reporting fewer perceived transition challenges, and spouses' social support was associated with reporting fewer perceived transition challenges. Spouses' perceived ability to manage stress, social support, and sense of community belonging were associated with Veterans reporting fewer perceived transition challenges, whereas only Veterans' sense of community belonging was associated with spouses reporting fewer perceived transition challenges. Discussion: Multiple interdependent psychosocial factors may be associated with Veterans and spouses experiencing challenges during the military-to-civilian transition, emphasizing the need for services and programs that can address the needs of both parties to promote mutual readiness for, and support during, this important period of change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".