Smiling Mind Mindfulness in Schools Program as a Classroom-Based Self-Regulation Intervention: A Case Study
Bibliographic record
Abstract
The number of Canadian children experiencing mental health concerns, including both internalizing and externalizing difficulties, continues to be on the rise. Coincidingly, the education system in Saskatchewan continues to experience strained resources. Thus, finding an efficacious, cost-effective, and accessible mental health intervention is vital. Both internalizing (e.g., anxiety, depression) and externalizing (e.g., hyperactivity, aggression) mental health in children are correlated with poor self-regulation. Recent reviews of the literature suggest mindfulness is a promising self-regulation intervention, particularly for clinical populations, as it targets the underlying neural mechanisms related to emotion dysregulation. The current case study aimed to provide insight into the potential value of a specific mindfulness intervention, Smiling Mind, within the context of the BALANCE classroom in Saskatoon, SK. The research questions were as follows: (a) How does incorporating a mindfulness intervention into a tier-three (high support) elementary school classroom routine affect the self-regulation (e.g., ability to appropriately manage thoughts, emotions and behaviour) of students with internalizing or externalizing mental health difficulties/disorders? (b) How does a mindfulness intervention help or hinder student readjustment to the classroom setting following a prolonged absence from school due to COVID-19? And (c) What opinions, attitudes, and feelings do the students have towards incorporating mindfulness into their school day? Data sources for this study included audiotaped semi-structured interviews, a self-report measure on self-regulation, and a Daily Recording Checklist. Semi-structured interviews were completed in place of direct observations due to the COVID-19 pandemic related restrictions and the requirement of completing the research virtually. Four methods of data analysis were employed in this case study: categorical aggregation, pattern identification, direct interpretations, and naturalistic generalizations. This in-depth process led to the formation of three main themes: The Smiling Mind Program: A General Overview; Students with Exceptionalities: “Mindful Considerations”; and Responsive Teaching and Pedagogical Considerations. Results from this research could influence educators as they attempt to meet the mental health needs of all their students within an inclusive classroom environment. Having one more tool in their professional toolboxes, like the Smiling Mind Program, can empower teachers while at the same time enhance the overall well-being of their students. Additionally, future researchers will benefit from seeing how completion of an intervention case study during the COVID-19 pandemic demands flexibility, creativity and determination. The need to pivot and adapt to changing public health or school division policies and directives became the norm during this innovative study.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".