Exploring the benefits of delivering arts-based mindfulness groups and methods in school settings
Bibliographic record
Abstract
Children and youth with significant mental health challenges often experience academic \nand social-emotional difficulties at school. Early intervention is linked to improved mental health \nand academic outcomes in school-aged youth. School boards across Ontario are adopting a \nuniversal mental health promotion model where group programming is delivered to full \nclassrooms as a way to enhance wellness in schools. For my advanced practicum, I implemented \nthe Holistic Arts-Based Program (HAP) within the Rainbow District School Board with support \nfrom various social workers, educators, and educational assistants. HAP is a group program that \nteaches youth mindfulness skills and concepts through arts-based and experiential methods. The \nHAP program is an enjoyable and engaging way to teach mindfulness to youth. My goal was to \nexplore the benefits of delivering arts-based mindfulness groups and methods in school settings. \nHAP proved to be an excellent program to implement in schools as a way of fostering student \nresilience, teaching coping skills, and building self-esteem, and self-awareness. Through artsbased and experiential methods, students can learn a variety of tools and strategies that will \nenhance their wellbeing. Delivering HAP in various schools allowed students to build \nconnections with peers and their teachers. HAP also promoted inclusion, diversity, empathy, and \nrespect for others. Art-based methods enabled students to express themselves and learn about one \nanother in a creative, meaningful, and interactive way. School administration should consider \nimplementing arts-based mindfulness groups in classrooms when selecting universal promotion \nprograms for their schools. Schools are the ideal place to deliver effective and strengths-based \ngroup work.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".