Convergent and divergent validity of the Connor-Davidson Resilience Scale in children with concussion and orthopaedic injury
Bibliographic record
Abstract
OBJECTIVE: Psychological resilience is an important predictor of outcome in concussion; however, there is no published evidence of psychometric support for use of a measure of psychological resilience in children with concussion. This study examined construct validity of the Connor-Davidson Resilience Scale (CD-RISC) in a paediatric concussion and orthopaedic injury sample. METHODS: Seventy-five children with a history of concussion or orthopaedic injury were recruited from a children's hospital. Total sample and within-group correlations between the CD-RISC (25- and 10-item versions) and measures presumed to be related or unrelated to the construct of psychological resilience were analysed. RESULTS: In the total sample, both versions of the CD-RISC were negatively correlated with self-reported depressive symptoms and general behaviour problems. The 10-item version was also negatively correlated with parent-reported general behaviour problems and self-reported anxiety, and was positively correlated with self-reported quality of life. The injury groups did not exhibit significantly different correlations. CONCLUSIONS: The construct validity of the CD-RISC is satisfactory when used with children with concussion. The 10-item version may provide a more efficient measure of resiliency with better construct validity in this population.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".