Psychological Resilience as a Predictor of Persistent Post-Concussive Symptoms in Children With Single and Multiple Concussion
Bibliographic record
Abstract
OBJECTIVES: To evaluate the relationship of psychological resilience to persistent post-concussive symptoms (PCS) in children with a history of single or multiple concussions, as well as orthopedic injury (OI). METHODS: Participants (N=75) were children, ages 8-18 years, who sustained a single concussion (n=24), multiple concussions (n=25), or an OI (n=26), recruited from a tertiary care children's hospital. All participants sustained injuries at least 6 months before recruitment, with an average time since injury of 32.9 months. Self-reported psychological resilience was measured using the Connor-Davidson Resilience Scale, and both self- and parent-reported PCS were measured using the Post-Concussion Symptom Inventory. Hierarchical regression analyses examined psychological resilience as a predictor of PCS, both as a main effect and as a moderator of group differences. RESULTS: Multiple concussions and low psychological resilience were both significant predictors of persistent PCS. Resilience was not a significant moderator of group differences in PCS. CONCLUSIONS: Sustaining multiple concussions may increase a child's risk of persistent PCS; however, high psychological resilience may serve as a protective factor, regardless of the number or type of injuries sustained. These findings provide support for developing and testing interventions aimed at increasing psychological resilience as a potential means of improving outcomes for children suffering from persistent PCS after concussion. (JINS, 2018, 24, 759-768).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".