Pioneering paths to positive mental health: an in-depth scoping review of multicomponent positive psychology group interventions for youth
Bibliographic record
Abstract
Interventions supporting youth mental health are crucial and multicomponent positive psychology interventions (MPPIs) show promise by targeting several promotive factors at a population-level. This scoping review provides synthesized recommendations based on existing knowledge and programs (n = 56) to bolster evidence-based practice. Findings revealed the most common MPPI components: cultivating strengths (65.38%), relationships (59.62%), and positive emotions (53.85%). Many studies measured outcomes pre- and/or post-intervention (both 92.00%), but follow-up assessments at or beyond one month were comparatively rare (38.00%). Further analysis explored participant information (age, gender, ethnicity, sample size), intervention delivery (sessions number/length, delivery location, activities, facilitator type), and additional aspects of evaluation (outcomes, control group, qualitative component). Notably, many sources provided insufficient data for key variables of interest (missing data for at least one variable in category ranged from 42.86% to 75.93%). Findings informed guidelines for PPI reporting and further offer valuable insights for educators, policymakers, practitioners, and researchers.
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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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".