Role of military context and couple well-being in the psychological vulnerability of military wives in the United States
Bibliographic record
Abstract
Introduction: Rooted in a socio-ecological and stress process framework, assumptions are that both distal contextual factors (military context) and more proximal interpersonal factors (couple well-being) have bearing on a person's psychological vulnerability. Methods: Using cross-sectional data from 222 U.S. military wives, this study explored how military context (specifically, number of deployments, perceived social support from the military community, and assessments of military life satisfaction) was associated with psychological vulnerability (depressive symptoms and personal well-being). Furthermore, the authors examined the mediating role of couple well-being, a latent variable composed of marital quality, relationship communication satisfaction, and relationship warmth. Results: A series of structural equation models indicated that social support from the military community was directly associated with higher levels of personal well-being among military wives. Additionally, wives who were more satisfied with military life tended to indicate higher levels of couple well-being; in turn, higher levels of couple well-being were associated with less psychological vulnerability for spouses through both lower levels of depressive symptoms and higher levels of personal well-being. Discussion: Results suggest that military contextual factors have some bearing on the psychological vulnerability of military wives, especially when spouses do not feel supported by the military community. Nonetheless, couple relationship appeared to be the most salient resource for combating wives' psychological vulnerability and, thus, a potential leverage point for intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".