The impact of deployment and traumatic brain injury on the health and behavior of children of US military service members and veterans
Bibliographic record
Abstract
This study examined the impact of service member/veteran (SMV) combat deployment and traumatic brain injury (TBI) on the health and behavior of his or her children. Participants were 104 female spouse caregivers of US SMVs who had sustained a mild, severe, or penetrating TBI. Participants completed the Children's Health and Behavior Questionnaire (CHBQ; r = .758 to .881) that evaluates school grades, behavior, medical health, emotional health, and social participation: (a) prior to the first combat deployment, (b) in the month prior to the TBI, (c) within 2 years after the TBI, and (d) 2 or more years after the TBI. A substantial number of children experienced a decline in health and behavior following the TBI (41.7%-79.1%). Of those who declined (a) 68.8%-75.5% declined within the first 2 years post-injury, followed by improvement or stabilization; (b) 6.7%-15.6% declined only after 2 or more years post-injury; (c) 15.6%-25.0% declined within the first 2 years post-injury and then again 2 or more years post-injury; and (d) 16.9%-26.5% experienced a decline as a result of deployment, followed by an additional decline after the SMV's TBI. Services are required for children of SMVs following TBI and deployment, particularly children at risk for poor outcome.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".