Predictors of Long-Term Psychosocial Functioning and Health-Related Quality of Life in Children and Adolescents With Prior Concussions
Bibliographic record
Abstract
OBJECTIVES: Individual differences in long-term psychosocial functioning after concussions in children and adolescents are poorly understood. The aim of the study was to investigate potential predictors of long-term psychosocial functioning and health-related quality of life in youth after prior concussion. METHODS: Participants (N=75; mean age=14.3 years old; 52% girls) with one prior concussion (n=24), multiple prior concussions (n=24), or a prior orthopedic injury and no concussion (n=27) were seen on average 2.7 years after their most recent injury. Psychosocial functioning was assessed using the self-report versions of the Behavior Assessment System for Children (BASC-2; Anxiety and Depression scales only), the Strengths and Difficulties Questionnaire, and the Pediatric Quality of Life Inventory TM 4.0. Pre-existing conditions (attention problems, learning difficulties, mood concerns, anxiety concerns, and migraines) were reported by parents using a checklist and examined as predictors of long-term functioning. Other potential predictors included age at testing, sex, time between most recent injury and testing, and number of prior concussions. RESULTS: The groups did not differ significantly on long-term psychosocial functioning. Moreover, only pre-existing mood concerns or attention problems significantly predicted psychosocial adjustment. CONCLUSIONS: Children's functioning before a concussion is critical to understanding outcome. Pre-injury attention and mood concerns should be assessed in clinical settings to prevent and treat long-term psychosocial problems after concussion. (JINS, 2018, 24, 540-548).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".