Relationship satisfaction among spouse caregivers of service members and veterans with comorbid mild traumatic brain injury and post‐traumatic stress disorder
Bibliographic record
Abstract
Abstract This study examined relationship satisfaction and health‐related quality of life (HRQOL) among spouse caregivers assisting service members and veterans (SMV) with comorbid uncomplicated mild traumatic brain injury (MTBI) and post‐traumatic stress disorder (PTSD). Spouse caregivers (N = 205) completed the Couples Satisfaction Index (CSI), 12 HRQOL measures, and the Mayo‐Portland Adaptability Inventory 4th Edition (MPAI‐4). T‐scores were classified as “clinically elevated” using a cutoff of ≥60T. The sample was also classified into “Satisfied” (≥13.5, n = 113, 55.0%) or “Dissatisfied” (<13.5, n = 92, 44.0%) relationship categories. Using stepwise regression analysis, Anxiety, Family Disruption, Vigilance, Emotional Support, Feeling Trapped, and MPAI‐4 Adjustment were identified as the strongest predictors of CSI total scores (p < 0.001), accounting for 41.6% of the variance. Squared semi‐partial correlations revealed that 18.1% of the variance was shared across all six measures, with 7.8% to 1.5% of unique variance accounted for by each measure separately. When comparing the number of clinically elevated measures simultaneously, the Dissatisfied group consistently had a higher number of clinically elevated scores compared to the Satisfied group (e.g., 3‐or‐more clinically elevated scores: Dissatisfied = 40.2%, Satisfied = 8.8%, OR = 6.93, H = 0.76). Caring for a SMV with comorbid TBI and PTSD can have a profound impact on the spouse caregiver's HRQOL, relationship satisfaction, and family functioning. The findings from the current study continue to support the need for family involvement in the SMV’s treatment plan, but more effort is needed to integrate behavioral health treatment that focuses on the family member's own issues into military TBI and PTSD systems of care.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".