A moderated-mediation analysis of pathways in the association between Veterans’ health and their spouse’s relationship satisfaction: The importance of social support
Bibliographic record
Abstract
Introduction: Military personnel and Veterans are at increased risk of mental and physical health conditions, which can impact their families. Spouses often perform a vital role in caring for service members and Veterans facing illness or injury, which can lead to caregiver burden. In turn, this may contribute to relationship issues. Research suggests that ensuring that spouses are well supported can alleviate some of these negative effects. The current study examined whether social support received by spouses of newly released Veterans buffers the impact of Veterans' health on caregiver burden, subsequently impacting spouses' relationship satisfaction. Methods: = 595 spouses of Regular Force Veterans who released in 2016 with at least 2 years of service. We examined Veterans' mental and physical health and spouses' caregiver burden, social support, and relationship satisfaction. A moderated mediation model was tested using structural equation modeling. Results: There was a significant indirect association between Veterans' health (both physical and mental) and spouses' relationship satisfaction through caregiver burden. Furthermore, social support moderated the association, as evidenced by a weaker association between Veterans' health and caregiver burden at low levels (-1SD) of social support compared to high levels (+1SD). Implications: Findings suggest additional efforts should be made to ensure sufficient support is provided to spouses, especially when they are caring for a service member or Veteran facing illness or injury, to strengthen their families' well-being.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".