The “what,” “why,” and “when” of using mindfulness in schools: Best practices and guidance for educators and policymakers
Bibliographic record
Abstract
Mindfulness-based interventions (MBIs) have recently proliferated among schools. Although MBIs show much promise in alleviating mental distress, their increase in school settings may outpace the evidence base for such implementation. In the present literature review, we define the different types of MBIs implemented in school settings: mindfulness-based stress reduction, mindfulness-based cognitive therapy for children, and mindfulness-based social-emotional learning programs. We next identify the outcomes most commonly addressed by MBIs in school settings, such as mental health, social-emotional, and academic outcomes. We conclude with a discussion of MBI implementation in schools and recommendations for educators and policymakers on best practices to maximize MBI effectiveness.
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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 | 0.065 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".