Resilience and symptom reporting following mild traumatic brain injury in military service members
Bibliographic record
Abstract
PRIMARY OBJECTIVE: The purpose of this study was to examine the relationship between resilience and symptom reporting following mild traumatic brain injury (mTBI). It was hypothesized that, as resilience increases, self-reported symptoms would decrease. RESEARCH DESIGN: Cross-sectional design. METHODS AND PROCEDURES: Participants were 142 US military service members who sustained a mTBI, divided into three resilience groups based on participants' responses on the Response to Stressful Experiences Scale: Moderate (n = 42); High (n = 51); and Very High (n = 49). Participants completed the Neurobehavioral Symptom Inventory (NSI) and PTSD Checklist-Civilian Version (PCL-C) within 12 months following injury. MAIN OUTCOMES AND RESULTS: There were significant main effects for the NSI total score, cognitive cluster and affective cluster, as well as for the PCL-C total score, avoidance cluster and hyperarousal cluster. Pairwise comparisons revealed that there was a negative relationship between resilience and self-reported symptoms overall. Specifically, participants with higher resilience reported fewer post-concussion and PTSD-related symptoms than participants with lower levels of resilience. CONCLUSIONS: These findings underscore the important role that resilience plays in symptom expression in military service members with mTBI and suggest that research on targeted interventions to increase resilience in the acute phase following injury is indicated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".