Problem-Solving Training as an Active Ingredient of Treatment for Youth Depression: A Scoping Review and Meta-Analysis
Bibliographic record
Abstract
Problem-solving (PS) training is a common ingredient of evidence-based therapies for youth depression that can be delivered as part of broader therapy packages or alone. As a stand-alone treatment, Problem-Solving Therapy (PST) is effective for adult depression. Aims: This scoping review aimed to provide an overview of the evidence concerning PS training for depression in youth aged 14 to 24. We searched five bibliographic databases and the grey literature. We included four randomized control trials (524 participants) comparing PST with waitlist controls or comparator interventions; four secondary analyses of PS-related concepts as predictors, moderators, or mediators of treatment response; 23 clinical practice guidelines (CPGs); and insights from a youth advisory panel. One high-quality study found PST helped with personal problems but was not significantly more effective than the control at reducing emotional symptoms. Exploratory evidence suggests PS training may enhance treatment effectiveness if provided alongside other skills in cognitive behavioural therapy. CPGs do not recommend PST as a first-line treatment for youth depression. Exploratory meta-analysis showed a small effect (Hedges’ g = -0.34; 95% CI: -0.92 to 0.23) with high heterogeneity and a very low quality of the evidence. After removing one study at high risk of bias, effect size and heterogeneity were strongly reduced (g = -0.08; 95% CI: -0.26 to 0.10). High-quality trials of PST alone or with other therapies are needed. As per suggestions by youth advisors, PS training may need to be reworked to ensure it is youth-driven, strengths-based, comprehensive, and personalized.
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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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".