Impact of MindUP Among Young Children: Improvements in Behavioral Problems, Adaptive Skills, and Executive Functioning
Bibliographic record
Abstract
Abstract Objectives We evaluated the impacts of a mindfulness-based social and emotional learning (SEL) program on behavioral problems, adaptive skills, and executive functioning among kindergarten students. Methods A total of 23 classrooms were assigned to the intervention group, in which the teachers implemented MindUP, and 19 classrooms were assigned to the comparison group, in which the teachers delivered their classes as usual. Teachers assessed the behavior of students (N = 584; intervention n = 261; comparison n = 323) both pre- and post-intervention with two measures: the Behavior Assessment System for Children, Third Edition, Teacher Rating Scales (BASC-3 TRS) and the Behavior Rating Inventory of Executive Function-Preschool and Child Version (BRIEF-P; BRIEF-2). Results Students who received the intervention demonstrated an improvement in adaptive skills and reduction in behavioral symptoms, internalizing composite, and externalizing composite outcomes. Additionally, there was a significant decrease in executive functioning deficits among students who participated in MindUP. There were no gender differences regarding changes in any of the five study outcomes. Conclusions The study suggests that mindfulness-based SEL intervention can improve psychosocial and behavioral outcomes in young children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".