Six decades of preventing and treating childhood anxiety disorders: a systematic review and meta-analysis to inform policy and practice
Bibliographic record
Abstract
Question Anxiety disorders are the most prevalent childhood mental disorders. They also start early and persist, causing high individual and collective costs. To inform policy and practice, we therefore asked: What is the best available research evidence on preventing and treating these disorders? Methods We sought randomised controlled trials (RCTs) evaluating interventions addressing anxiety problems in young people. We identified RCTs by searching CINAHL, ERIC, MEDLINE, PsycINFO and Web of Science. Thirty-three RCTs met inclusion criteria—evaluating 8 prevention programmes, 12 psychosocial treatments and 7 pharmacological treatments. We then conducted meta-analyses by intervention type. Findings For prevention, the cognitive-behavioural therapy (CBT) programme Coping and Promoting Strength stood out for reducing anxiety diagnoses. For psychosocial treatment, 9 CBT interventions also reduced diagnoses: Cool Kids ; C ool Little Kids Plus Social Skills; Coping Cat; Coping Koala; One-Session Treatment; Parent Education Program; Skills for Academic and Social Success; Strongest Families and Timid to Tiger . Successful CBT interventions were used with children ranging from pre-schoolers to teens in homes, communities/schools and clinics. For pharmacological treatment, selective-serotonergic-reuptake-inhibitors (SSRIs) significantly improved symptoms. Fluoxetine stood out for also reducing post-test diagnoses, but caused adverse events. Meta-analyses indicated strongest effects for CBT (Log OR=0.95; 95% CI, 0.69 to 1.21) and SSRI treatments (1.57; 1.09 to 2.06). Conclusions CBT is effective for preventing and treating childhood anxiety—across a range of ages and formats. Fluoxetine is also an effective treatment but side effects must be managed. CBT prevention and treatment interventions should be made widely available, adding fluoxetine in severe cases.
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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 | 0.055 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".