Factors influencing postdeployment reintegration adjustment for U.S. service members and their spouses by spouse gender
Bibliographic record
Abstract
Research on spouses' adjustment after military deployment has focused primarily on female spouses of male service members; little is known about how adjustment differs by gender. We used Walsh's family resilience framework to examine communication, belief system, organizational factors, and other stressors, likely associated with postdeployment adjustment. Using Millennium Cohort Family Study data, logistic regressions assessed risk and protective factors on spouses' and service members' time to adjust, exploring whether spouse gender moderated their associations. Findings indicated that the association of (1) spouses' perceptions of their own mental functioning with spouses' and service members' adjustment and (2) spouses' mental readiness for deployment with service members' adjustment both differed by spouse gender, with associations attenuated for male spouses and their service member partners. Other factors associated with family adjustment included the spouse's satisfaction with communication, the extent to which the service member shared deployment experiences, the extent to which the spouse was bothered by deployment experiences, the spouse's participation in postdeployment transition programs, the spouse's informal support during deployment, and length of deployment. Results indicated shared and gender-specific risk and protective factors associated with spouse and service member adjustment, demonstrating the importance of tailored military family support programs addressing the needs of different populations of military spouses.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".