Psychological Resilience as a Predictor of Symptom Severity in Adolescents With Poor Recovery Following Concussion
Bibliographic record
Abstract
OBJECTIVES: Examine the mediating effects of anxiety and depressive symptoms on the relationship between psychological resilience and post-concussive symptoms (PCS) in children with poor recovery following concussion. PARTICIPANTS AND METHODS: Adolescents (N=93), ages 13 to 18 years, were assessed at a neuropsychology screening clinic at a children's hospital. They sustained concussions more than 1 month before the clinic visit (median time since injury=5.1 months; range=42-473 days) and were seen on the basis of poor recovery (i.e., presence of persistent PCS and complaints of cognitive problems). Self-reported psychological resilience was measured using the 10-item version of the Connor-Davidson Resilience Scale; self- and parent-reported anxiety and depressive symptoms were measured using the Behaviour Assessment System for Children - Second Edition; and self- and parent-reported PCS were measured using the Post-Concussion Symptom Inventory. All variables were measured concurrently. Regression-based mediation analyses were conducted to examine anxiety and depressive symptoms as mediators of the relationship between psychological resilience and PCS. RESULTS: Psychological resilience significantly predicted self-reported PCS. Self-reported anxiety and depressive symptoms significantly mediated the relationship between resilience and self-reported PCS, and parent-reported child depressive symptoms significantly mediated the relationship between resilience and self- and parent-reported PCS. CONCLUSIONS: Psychological resilience plays an important role in recovery from concussion, and this relationship may be mediated by anxiety and depressive symptoms. These results help shed light on the mechanisms of the role of psychological resilience in predicting PCS in children with prolonged symptom recovery. (JINS, 2019, 25, 346-354).
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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 | 0.001 |
| 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".