Veterans’ social–emotional and physical functioning informs perceptions of family and child functioning
Bibliographic record
Abstract
Introduction: Veteran-connected families and children are an understudied population who may experience a host of stressors, including exposure to disabling parental injury, unstable family income, changes in peer support networks, and a civilian community that is less aware of their particular needs. Using a systems perspective, this article examines the association between Veterans’ social–emotional and physical functioning deficits and perceptions of family and child functioning. Methods: Participants were 594 male Veteran parents who completed the Chicago Veterans Survey, including the World Health Organization Disability Assessment Schedule, the McMaster Family Assessment Device, and a child functioning screening tool. Results: Structural equation models indicated positive direct effects of Veteran functioning deficits on perceptions of adverse family and child functioning. Veteran functioning also had indirect effects on perceived child functioning through family functioning in social and physical models (βs = 0.065 and 0.055, ps = 0.017 and 0.006, respectively). Discussion: In both social–emotional and physical functioning models, increases in Veteran functioning deficits were associated with poorer perceptions of family functioning and more negative reports of child outcomes. Although many Veteran families appear resilient, prevention and intervention services targeting family functioning may be a useful strategy to interrupt cascading negative effects of Veterans’ health deficits. Demonstrating these relationships in a Veteran context is critical to developing policies and programs that effectively support Veteran-connected 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".