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
Abstract Background: Prevention programs for children such as mindfulness-based interventions (MBIs) are often implemented in schools to prevent psychological disorders from emerging and contribute to children’ mental health. Aim: This study used a randomized cluster design and assessed the impact of a MBI on well-being and mental health of elementary school children’s. Method: 13 elementary school classrooms 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. Results: Regression analyses revealed significant differences between conditions for inattention. Participants in the MBI condition reported no changes in pre- to post-intervention scores, whereas participants from the control conditionreported increases in scores. Results also showed significant differences in competence scores. Participants in the MBI condition reported a non-significant decrease in competence scores, whereas participants in the control condition reported significantly higher competence scores from pre- to post-intervention. Discussion: Results do not indicate that the MBI had a significant impact on children’s well-being and mental health. This suggests MBIs may not have an added value when compared to other preventive interventions geared towards well-being and mental health promotion in school settings.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".