Intimate partner distress is strongly associated with worse warfighter brain health following mild traumatic brain injury.
Bibliographic record
Abstract
OBJECTIVE: To examine (a) change in chronic neurobehavioral symptoms in service members/veterans (SMVs) with an uncomplicated mild traumatic brain injury (MTBI) at two time points over 3 years and (b) the influence of intimate partner (IP) health-related quality of life (HRQOL) risk factors for chronic neurobehavioral symptoms. METHOD: = 175) completed measures of SMV neurobehavioral adjustment symptoms and 13 IP HRQOL risk factors at Time 1 (T1) ≥ 12 months post-TBI and Time 2 (T2) 3 years later. Scores on the risk factor measures were classified into four IP HRQOL symptom trajectory categories based on clinically elevated (≥ 60 T) symptoms: (a) persistent (T1 + T2 ≥ 60T), (b) developed (T1 < 60T + T2 ≥ 60T), (c) improved (T1 ≥ 60T + T2 < 60T), and (4) asymptomatic (T1 + T2 < 60T). RESULTS: There was little change in mean SMV adjustment scores or the percentage of clinically elevated scores from T1 to T2. The percentage of clinically elevated adjustment scores was 30% at T1 and T2; 14.3% at T1 only; and 5.7% at T2 only. The IP HRQOL symptom trajectories had a stronger effect on mean SMV adjustment than within-group change in adjustment, which was largely driven by the persistent and asymptomatic IP HRQOL categories. The strongest effects were found for caregiving and social HRQOL risk factors, followed by psychological, and then physical HRQOL risk factors. CONCLUSION: A range of clinically elevated IP HRQOL constructs emerged as long-term risk factors for chronic neurobehavioral symptoms in SMVs post-MTBI. More attention to the role that family distress has on poor warfighter recovery and return to duty following an MTBI is required. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".