A mixed‐methods systematic review of the effects of mindfulness on nurses
Bibliographic record
Abstract
AIM: To review the effects of mindfulness-based interventions on Registered Nurses and nursing students. BACKGROUND: Work-related stress among nurses is estimated to be the biggest occupational health problem after musculoskeletal disorders. DESIGN: A mixed-method systematic review incorporating quantitative and qualitative data was conducted. DATA SOURCES: Studies on the effects of mindfulness-based interventions for nurses and nursing students published between 1980 and 2014 were identified through a systematic search in electronic databases: Medline, Embase, PsycINFO, Cochrane Library and Cinahl. REVIEW METHODS: Data analysis was conducted based on the framework of Thomas and Harden (2004). RESULTS: A total of 32 studies, including 17 controlled designs, 11 pre-post designs and four qualitative designs were reviewed. Meta-analysis suggests that mindfulness-based interventions may be effective in significantly reducing state anxiety and depression at posttreatment and state anxiety and trait anxiety at follow-up. Qualitative studies and uncontrolled studies shed light on benefits overlooked in RCTs, including improvements in the well-being of individuals (e.g. inner state of calmness, awareness and enthusiasm) and improved performance at work (better communication with colleagues and patients, higher sensitivity to patients' experiences, clearer analysis of complex situations and emotional regulation in stressful contexts). CONCLUSIONS: Mindfulness appeared to improve nurses' mental health significantly. It could be used in worksite health promotion programmes. Only a few studies have explored the impact of mindfulness on nurses' professional behaviours and their relationships with patients and colleagues. Future research should further explore the long-term impacts of mindfulness on performance and well-being at work using sound methodological designs.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".