Investigating the effects of meditation technologies on nursing student stress and anxiety: Results of a pilot feasibility study
Bibliographic record
Abstract
• Nursing students report experiencing high levels of stress and anxiety. • Technology-assisted meditation is a strategy to help reduce nursing student stress and anxiety. • Perceived stress and state anxiety decreased significantly for nursing students who used the MUSE EEG biofeedback headband for meditation practice. Nursing students report high levels of stress and anxiety. Meditation technologies may facilitate meditation and improve students' mental well-being. To assess feasibility and efficacy of two meditation technologies—Headspace mobile app and MUSE biofeedback headband—on nursing students' stress and anxiety. In this pilot study, 44 undergraduate nursing students were randomly assigned to either the Headspace or MUSE group, completing two 10-minute meditation sessions weekly for five weeks. Stress and anxiety were measured using the perceived stress scale (PSS), state-trait anxiety inventory (STAI), and social interaction anxiety scale (SIAS). Acceptability was measured using the technology acceptance model survey (TAMS). Both groups showed decreases in perceived stress and state anxiety, with significant reductions for MUSE participants ( p =.025; p =.005). Headspace was perceived as more accessible than MUSE ( p =.001). Findings suggest meditation technologies are feasible and acceptable for nursing students, with time and motivation as barriers. MUSE neuromeditation showed promising results in reducing perceived stress and anxiety. Study results support need for larger trials exploring the impact of meditation technologies on nursing students' well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".