Impact of a mindfulness-based intervention on well-being and mental health of elementary school children: results from a randomized cluster trial
Bibliographic record
Abstract
Prevention programs, such as mindfulness-based interventions (MBIs), are often implemented in schools to prevent psychological disorders from emerging in children and to support their mental health. This study used a randomized cluster design to evaluate the impact of a MBI, called Mission Méditation, on the well-being and the mental health of elementary school children's. 13 classrooms of an elementary school were randomly allocated to the experimental condition (7 classrooms, n = 127 students) or the waitlist control condition (6 classrooms, n = 104 students). Participants in the experimental condition received a 10-week MBI. Regression analyses revealed significant differences between conditions for inattention. Participants in the MBI condition reported no change in pre- to post-intervention, whereas participants in the control condition reported pre- to post-intervention increases. Results also showed significant differences in perceived competence. Participants in the MBI condition reported a non-significant decrease in perceive competence, whereas participants in the control condition reported significantly higher perceive competence scores from pre- to post-intervention. Results do not indicate that the MBI had a significant impact on participant's well-being and mental health. This suggests that MBIs may not have an added value when compared to other preventive interventions geared towards well-being and mental health promotion in school settings.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".