Effects of Age and Subtype on Emotional Recognition in Children with Anxiety Disorders: Implications for Cognitive-Behavioural Therapy
Bibliographic record
Abstract
OBJECTIVE: It remains unclear whether an anxiety diagnosis is associated with children's emotional recognition. We considered children's age and types of primary anxiety diagnosis, which have been neglected, to elucidate this relationship. METHODS: Sixty-three referred children with anxiety disorder(s) and 59 volunteer children without anxiety disorder(s), aged between 6 and 11 years, were presented with animated characters, displaying a range of simple and complex emotions, for identification. Statistical analyses examined identification accuracy based on presence or absence of anxiety disorder, age, and types of primary diagnoses. RESULTS: Children with anxiety disorder(s) as a group performed comparably to children without anxiety disorder(s) in identifying emotions (z = -0.72, P = 0.47). In both groups, accuracy for disgust increased significantly each year of age ([anxiety group] OR 2.6; 95% CI 1.6 to 4.3, P < 0.001, [control group] OR 2.1; 95% CI 1.3 to 3.3, P = 0.002). When primary anxiety types were considered, while controlling for age, children with separation anxiety disorder (SAD) showed deficits in overall emotional recognition, compared with children with other subtypes or without anxiety (P = 0.004). Further regression analyses showed that children with generalized anxiety disorder (GAD) presented significantly lower accuracy than children without anxiety disorder(s) at a young age, but the deficit disappeared with increased age. CONCLUSION: Children with anxiety disorder(s) as a group may not appear to be impaired in emotional recognition. However, when age and subtypes are considered, children with SAD and young children with GAD appear to have difficulty, compared with children without anxiety disorder(s).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".