Prevalence and Treatment of Anxiety Disorders in Children and Adolescents
Bibliographic record
Abstract
Using meta-analysis and physician billing data, the prevalence and efficacy of treatments for anxiety disorders in children and adolescents was elucidated. To use billing data from the Calgary Health Region (CHR) to determine anxiety disorder prevalence rates. Meta-analysis was performed to compare treatment efficacy of two leading interventions for anxiety disorders in youth. We sought to examine the prevalence of anxiety disorders in the CHR, and evaluate the efficacy of cognitive behavioural therapy (CBT) and attention bias modification (ABM) for the treatment of anxiety disorders in youth. Physician billing data was used to identify 303 938 unique individuals diagnosed with an anxiety disorder. Effect sizes for CBT/ABM treatments were calculated for clinician and self-report measures and displayed in forest plots, allowing comparison of treatment efficacy. The 16-year cumulative anxiety disorder prevalence rate was 28.8% in the CHR, while the annual rate increased from 1.5% to 2.1% from 1994 to 2009. There was no evidence of efficacy for the ABM treatment. 80% of the CBT studies showed efficacy of the treatment as rated by clinicians, but none showed significant improvement in self-report measures. Analysis revealed the ABM placebo was more effective than ABM, and significantly more efficacious than CBT. The prevalence of anxiety disorders in the CHR increased by 40% from 1993 to 2009. Meta-analysis revealed no evidence of efficacy for ABM. CBT only showed efficacy in terms of clinician-rated measures; possible evidence of clinician bias. Ultimately, revision of current ABM methods is necessary.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.028 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".