The effect of a mindfulness based stress reduction intervention on the perceived stress and burnout of RN students completing a doctor of nursing practice degree
Bibliographic record
Abstract
Background and objective: There is a vast amount of literature documenting the epidemic of stress and burnout within the nursing profession. It is well established that chronic stress contributes to burnout among nursing staff and students. Research suggests that organizational change, curriculum adjustment, and mindfulness interventions can contribute to decreased stress and better outcomes for nurses. The objective of this study was to investigate the effect of a Mindfulness Based Stress Reduction (MBSR) intervention on the perceived stress and burnout of students in a cohort of Registered Nurses (RNs) completing a Doctor of Nursing Practice (DNP) Degree.Methods: This study utilized pre and post data collection to explore the effect of a MBSR intervention on self-reported perceived stress and burnout using the Perceived Stress Scale (PSS) and Copenhagen Burnout Inventory (CBI). Study participants (n = 24) received a general orientation to the study followed by a brief intervention using the body scan meditation, a component of the MBSR-model. Students registered with the Remind mobile app to supplement the live instruction and to encourage the students to engage in daily mindfulness practice.Results: The repeated measures ANOVAs for all three CBI factors showed that personal, work, and client burnout means were statistically lower at four weeks post-intervention than they were at baseline. Perceived stress measures four weeks post-intervention were also statistically lower than baseline. There were no demographic interactions, and only one main effect for gender, in that males reported lower perceived stress. Conclusions: The MBSR intervention was successful in reducing the self-reported perceived stress and burnout of RN students completing their DNP Degree.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".