Mindful practices to support university faculty sense of wellbeing and enhance their teaching-learning scholarship: a mixed-method pilot study
Bibliographic record
Abstract
Teaching-learning approaches of university faculty increasingly include supporting their own wellbeing, as well as that of their students. Engaging in mindful practices has the potential to increase faculty capacity for reflexivity and compassion, which they can incorporate into their teaching-learning. However, few faculty have knowledge and skill of such practices. The aim of this mixed-method study was to pilot-test a mindfulness intervention designed to build faculty capacity for mindful practices. Faculty from an urban university in Canada participated in a three-workshop series on mindful practices, which they were then encouraged to practice and bring into their classrooms. Data collection included pre and post-test measures of mindfulness, wellbeing and self-compassion. Qualitative focus-group interviews were conducted at the end of the study. Mindfulness scores significantly increased. While wellbeing and self-compassion scores also improved, the change was not statistically significant. Faculty described potential benefits and barriers to applying mindful approaches in the classroom. Mindful practices were well received by faculty and show promise in supporting their wellbeing and the quality of their teaching-learning. Further research is required to address how best to position faculty to engage in mindful practices in a sustainable way to also enhance the quality of students’ teaching-learning experience.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".