Improving Student Well-being in Education: Incorporating Mindfulness into Elementary Classrooms
Bibliographic record
Abstract
Mindfulness is the practice of present moment awareness without judgement. This research study aims to illuminate why mindfulness should be infused into Ontario elementary classrooms, and how teachers can incorporate this practice. This qualitative research study investigates the ways teachers are practicing mindfulness in their classrooms and the perceived impacts it has on the life experiences of both students and teachers. It identifies some of the barriers that teachers face when implementing mindfulness in the classroom. The existing peer reviewed literature on mindfulness practices with children show that it has the ability to improve neurological processes, and relieve physical health issues while supporting Ontario Ministry of Education’s goals to decrease and prevent mental health issues. Literature on mindfulness indicates that evident changes occur following this practice which can contribute to improving student well-being, and in turn, learning. The changes include, but are not limited to: improved classroom management, improved school culture, and improved student ability to learn in a positive manner. Data for this study was collected through semi-structured interviews with three Ontario elementary teachers. This study discusses the qualities of a mindful teacher and draws connections between mindfulness and current Ontario elementary curriculum. Lastly, this study provides implications and recommendations for the next steps on using mindfulness as a way to support student well-being in education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".