Clinical Nurse Well-being Improved Through Transcendental Meditation
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of Transcendental Meditation® (TM®) practice on the multidimensional well-being of nurse clinicians affected by the COVID-19 pandemic. BACKGROUND: The health of clinical nurses has substantial impact on both the availability of a nursing workforce and the quality and safety of patient care. TM improved health and coping strategies across many populations. METHODS: Clinical nurses were recruited from 3 Magnet®-designated hospitals during the COVID-19 pandemic. Well-being outcomes included flourishing, burnout, anxiety, and posttraumatic stress disorder. Participants were randomized following completion of baseline surveys into immediate (intervention) or delayed (control) TM instruction. Surveys were repeated at 1 and 3 months following baseline survey or TM instruction. Repeated-measures analysis of variance compared differences in groups over time. RESULTS: Across the 3 sites, there were 104 clinical nurse participants. Repeated-measures analysis of variance showed significant medium to large effects in improvement over time in well-being measures for the intervention group. CONCLUSIONS: TM improved multidimensional well-being of clinical nurses by reducing posttraumatic stress disorder, anxiety, and burnout and improving flourishing. TM is easy to practice anywhere. The benefits are immediate and cumulative. Organizations and individual nurses can use TM to support clinical nurses in the difficult and meaningful work of patient care, especially in challenging times. Future studies may consider the feasibility of integrating TM into clinical shifts and evaluating its impact on patient and organizational outcomes.
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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 | 0.000 |
| 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".