Meaning mindset theory: a transdiagnostic approach to mental health promotion and intervention for children
Bibliographic record
Abstract
A transdiagnostic approach is increasingly recognized as crucial in the prevention or treatment of child internalizing and externalizing mental health concerns. There is substantial overlap and comorbidity among various mental health concerns and the onset of one mental illness elevates the risk of others, underscoring the potential limitations of singular-focused mental health education or treatment. Meaning Mindset Theory (MMT) is a transdiagnostic framework developed and evaluated over the past decade in Canada as a promising new approach. MMT emphasizes agency over thoughts and behaviors, empathy and social competence skills, and meaningful engagement to enhance resilience for both internalizing and externalizing symptoms. The DREAM Program-Developing Resilience through Emotions, Attitudes, and Meaning is a mental health education program grounded in MMT principles. This program has enhanced meaning mindset-agency over thoughts and behaviors, hope for a future that is good, positive self-concept, and openness to learning, new experiences, and feelings-as well as both internalizing and externalizing mental health. To date, the DREAM program, as well as MMT more broadly, has been tested in diverse populations with school-aged children, families, neurodiverse and intellectually gifted young people, homeless men, and Black families, among others. Future research should explore the efficacy of an MMT in therapeutic settings compared to standard treatments, potentially enhancing mental health intervention strategies for Canadian children and families.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.006 |
| 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".