Relative Effectiveness of Online Cognitive Behavioural Therapy with Anxious or Depressed Young People: Rapid Review and Meta-analysis
Bibliographic record
Abstract
Global estimates suggest that 25% and 20% of youth have reported elevated symptoms of depression and anxiety, respectively, since the beginning of the COVID-19 pandemic compared to baseline functioning (Racine et al., 2021). Cognitive behavioural therapy (CBT) has been found to significantly benefit young people experiencing anxiety and depression (Christ et al., 2020). Pandemic-related protocols have led many mental health services to shift to online platforms. We wondered about the comparative efficacy of online versus offline CBT for young people between the ages of 10–25. We responded with a rapid review and meta-analysis of eight randomised controlled trial (RCT) outcomes. The sample-weighted, between-group effect size, the standardised mean difference (d), was essentially zero at longest follow-up (nine months), indicating that online and offline CBT were equally effective for youth with depression and anxiety; both online and offline groups’ symptom alleviation rates were approximately 90%. Recognising a lack of diversity in the samples has led us to argue that comparative meta-analyses across the most potentially vulnerable minoritised groups should be a focus in future research. This would help social workers and allied mental health providers support diverse clients and decision makers navigate the troubled clinical and social policy waters of the pandemic and its aftermath.IMPLICATIONS Online CBT seems as effective as offline CBT in alleviating anxiety and depressive symptoms among young people, including young adults beginning to develop symptoms.A more socially, racially, and gender-inclusive RCT-based synthetic research agenda is needed to steer online versus offline decisions in social work and allied mental health.Social work’s emphasis on diversity, equity, and inclusion has, with the focus of rigorous research, the potential to play an important role in addressing gendered, cultural, and socioeconomic knowledge gaps.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".