Relationship satisfaction among spouse caregivers of service members and veterans with comorbid mild traumatic brain injury and post‐traumatic stress disorder
Why this work is in the frame
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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 4 th 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it