The impact of a children's literature-based cognitive behavioural therapy skills curriculum on middle-school youth who self-report clinically relevant and non-clinical mental health symptomatology
Bibliographic record
Abstract
To evaluate potential differences in the impact of a children's literature-based mental health literacy and cognitive behavioural therapy (CBT) skills curriculum on middle-schoolers with and without mental health symptoms that may be clinically relevant. Youth (aged 11–14; grades 7–8) who received a 3-month teacher-delivered intervention embedded in the language arts curriculum (N = 196) were compared to a wait-list control group (N = 233) from a large, diverse, urban school board in Canada. The Revised Child Anxiety and Depression Scale (RCADS) and Life Problems Inventory (LPI) were administered. Youth were divided according to scores of possible clinical relevance into "clinical" and "non-clinical" groups. A mixed ANOVA (Intervention and Clinical status at baseline are between subject scores and Time is the only repeated measure) was used clustered by classroom. Thirty-three-point six percent of all participants endorsed symptoms of potential clinical relevance on the RCADS and/or one of its subscales. The primary analyses were non-significant but also underpowered to detect outcomes (RCADS β = 0 0.14; LPI β = 0.28). LPI scores improved numerically for intervention vs. control youth (clinical: -8% vs. +24%; non-clinical -9% vs. 0%, effect size = 0.002). This study was underpowered; however, it did identify a potentially clinically meaningful trend in a measure of maladaptive coping (LPI) favouring the intervention. A future, well-powered study is needed to characterize the impact of this intervention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".