Can a teacher-led mindfulness intervention for new school entrants improve child outcomes? Protocol for a school cluster randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: The first years of school are critical in establishing a foundation for positive long-term academic, social and well-being outcomes. Mindfulness-based interventions may help students transition well into school, but few robust studies have been conducted in this age group. We aim to determine whether compared with controls, children who receive a mindfulness intervention within the first years of primary school have better: (1) immediate attention/short-term memory at 18 months post-randomisation (primary outcome); (2) inhibition, working memory and cognitive flexibility at 18 months post-randomisation; (3) socio-emotional well-being, emotion-regulation and mental health-related behaviours at 6 and 18 months post-randomisation; (4) sustained changes in teacher practice and classroom interactions at 18 months post-randomisation. Furthermore, we aim to determine whether the implementation predicts the efficacy of the intervention, and the cost effectiveness relative to outcomes. METHODS AND ANALYSIS: This cluster randomised controlled trial will be conducted in 22 primary schools in disadvantaged areas of Melbourne, Australia. 826 students in the first year of primary school will be recruited to detect between groups differences of Cohen's d=0.25 at the 18-month follow-up. Parent, teacher and child-assessment measures of child attention, emotion-regulation, executive functioning, socio-emotional well-being, mental health-related behaviour and learning, parent mental well-being, teacher well-being will be collected 6 and 18 months post-randomisation. Implementation factors will be measured throughout the study. Intention-to-treat analyses, accounting for clustering within schools and classes, will adopt a two-level random effects linear regression model to examine outcomes for the intervention versus control students. Unadjusted and analyses adjusted for baseline scores, baseline age, gender and family socioeconomic status will be conducted. ETHICS AND DISSEMINATION: Ethics approval has been received by the Human Research Ethics Committee at the University of Melbourne. Findings will be reported in peer-review publications, national and international conference presentations and research snapshots directly provided to participating schools and families. PRE-RESULTS TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12619000326190).
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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 | 0.037 | 0.035 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.075 | 0.012 |
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".