Teaching for well-being? Introducing mindfulness in an undergraduate course
Bibliographic record
Abstract
Student mental health and well-being are increasing concerns in higher education. This exploratory study examined students’ learning in a mindfulness programme incorporated into an undergraduate class. Six brief mindfulness-based practices were introduced: mindfulness meditation, walking meditation, body scan, mindful eating, loving-kindness and Tonglen meditation. Fourteen students were interviewed after completion of the course to explore their conceptions and use of mindfulness and other self-care practices. Results of thematic analyses suggest that there were variations in students’ adoption of mindfulness-based practices and students’ preferred mindfulness techniques. Most participants reported that mindfulness instruction and practice were beneficial but this was not universal; some students reported that mindfulness was ‘not for them’. Findings suggest that mindfulness fostered self-reflection, self-awareness and relaxation for many students and incorporating mindfulness at the beginning of class improved the overall quality of discussion and facilitated students’ learning. Findings further suggest that students incorporated mindfulness into existing self-care practices that included a variety of other preferred techniques to enhance self-reflection, self-awareness and relaxation. Overall, our findings suggest that students should be helped, through explicit instruction, to develop a ‘toolbox’ of self-care approaches that may include, but are not necessarily limited to, mindfulness techniques.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".