Mindfulness-Based Interventions for Undergraduate Nursing Students in a University Setting: A Narrative Review
Bibliographic record
Abstract
(1) Introduction: Undergraduate (UG) nursing students are vulnerable to stress throughout their education, known to result in burnout, with high attrition rates of up to 33%. There is a growing body of evidence to suggest that mindfulness-based interventions are effective for the management of anxiety, depression and wellbeing, thereby reducing stress in healthcare provider populations. The aim of this narrative review was to synthesize and provide a critical overview of the current evidence in relation to mindfulness-based interventions for UG nursing students in a university setting. (2) Methods: A review of the literature was conducted in March 2020 and updated in May 2021, utilising the databases CINAHL, Medline and PsycINFO. (3) Results: Fifteen studies were included in the review, with three common themes identified: (i) the positive impact of mindfulness on holistic wellbeing, (ii) mindfulness-based techniques as a positive coping mechanism within academic and clinical practice, and (iii) approaches to the delivery of mindfulness-based interventions. (4) Conclusions: Mindfulness-based interventions are effective strategies for the management of stress, development of self-awareness and enhanced academic and clinical performance in undergraduate nursing students. No ideal approach to delivery or duration of these interventions was evident from the literature. Best practice in relation to delivery of mindfulness-based interventions for nursing students is recommended for future studies.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".