Mindfulness in medical education: Students’ perceptions and four recommendations for implementation of a mindfulness intervention
Bibliographic record
Abstract
Background: Faculties of Medicine around the globe have implemented mindfulness-based curricula to deal with medical student’s burnout, anxiety and depression. The purpose of this qualitative study is to assess students’ perception of a mandatory mindfulness intervention and their recommendations for further curricula development and implementation.Methods: Third-year medical students participated in a mandatory three-hour mindfulness workshop embedded in their family medicine academic week. Eleven students consented to two interviews which explored their perceptions of mindfulness and the workshop in relation to their personal and professional wellbeing as well as their views for the implementation of a longitudinal mindfulness curriculum.Results: Student and institutional benefits and barriers relating to the curriculum were identified. Student’s benefits included positive changes in stress, self-awareness and personally that also translated into self-reported better patient care. Students reported lack of time, forgetting to practice and lack of knowledge about mindfulness as barriers. Institutional pride for their support of student wellness and an overfilled curriculum, were the major institutional benefits and barriers respectively, to the expansion of this curriculum. Among developing an implementing a longitudinal mindfulness curriculum, we found four key features to consider: Firstly to engage the stakeholders; secondly, to incorporate the mindfulness intervention into the curriculum with both a mandatory and elective component; thirdly, to emphasize the clinical implications of the mindfulness intervention and fourthly, to have protected time for wellness interventions.Conclusions: Introducing mindfulness into the undergraduate medical school curriculum through this workshop resulted in perceived personal, institutional and professional benefits. For faculties of medicine that want to implement a mindfulness intervention, we found four key components for implementing a mindfulness intervention in their institutions. Further research is needed to better quantify the benefits and to identify ways to manage barriers at both individual and institutional levels.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".