Coming home: the experiences and implication of reintegration for military families
Bibliographic record
Abstract
Introduction: Although military families are typically resilient in the face of adversity, the current literature suggests that the aftermath of deployment involves numerous stressors and difficulties for these families for a long period. Method: Using a sample of 380 US service members, 295 partners of US service members, and 136 adolescents who experienced a full deployment cycle of a service member parent, this study addresses the gaps in knowledge by examining how factors identified in prior research (reintegration stress and coping, preparation and expectations, family functioning and parental satisfaction, perceived adolescent changes between deployment and reintegration, and adolescents’ perception of family functioning) affect reintegration stress and coping for US service members, partners of US service members (someone who identifies as being in a significant relationship with a service member), and adolescents. Results: Better service member coping, satisfaction with family deployment coping, better preparation, and accurate expectations were all found to be associated with lower reintegration stress. Discussion: Findings point to the need for a systemic approach throughout the deployment cycle for better reintegration outcomes for military individuals and families.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".