A Narrative Inquiry into the Experiences of Nursing Faculty Engaging in Mindful Practices
Bibliographic record
Abstract
Mindful practices have been found to decrease levels of stress, anxiety, and burnout, while enhancing resilience and coping in those who engage in them. The beneficial effects of mindfulness on improving physiological and psychological well-being are well supported within the literature. However, little has been done to learn how faculty in higher education, specifically in nursing, may experience the effect of mindful practices. The purpose of this study was to explore nursing faculty perceptions of how mindfulness may influence their teaching-learning practices as well as their daily lives. Fourteen full-time and part-time nursing faculty participated in a three-part mindfulness training workshop series delivered over 6 weeks. Upon completing the workshop series, seven of the 14 nursing faculty participated in a focus-group interview about their experiences engaging in the mindful activities during and in-between the workshops. Clandinin and Connelly’s (2000) narrative inquiry method was used to analyze participant accounts. This process entailed crafting a composite story that was sent to the participants for member checking. The composite story was analyzed using the three levels of justification: personal, practical, and social. Two significant patterns emerged: (1) Integrating mindful practices into personal life, and (2) Integrating mindful practices into the teaching and learning situations. The findings from this study contribute to the growing body of knowledge on how engaging in mindful practices may enhance the personal and the professional well-being of faculty in higher education, and thus support their teaching-learning encounters.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".