Intentional Self-Care Activities Improve Health Behavior Scores for Nurses: A Qualitative Observational Study
Bibliographic record
Abstract
AIMS AND OBJECTIVES: Nurses face a variety of stressors and research examining nursing employee wellness programs is inconclusive with regard to cost-benefit and outcomes. This study aims to explore the impact of a self-directed short-term (2 wk) self-care activity on registered nurses (RNs) self-rated health-related quality of life. METHODOLOGICAL DESIGN AND JUSTIFICATION: This was a prospective nonrandomized interventional pilot study that consented RNs working in a university hospital working day shift and night shift. Demographic data and the RAND-36-item Short Form (RAND-36) scores were obtained at baseline. Each RN then self-selected and committed to engaging in one of 30 self-care activities for a 2-week period. Follow-up RAND-36 scores were obtained after RNs completed the 2-week intervention. INSTRUMENT: The RAND-36 is a tool that was used to collect participant responses. RESULTS: One hundred twenty-one nurses were enrolled in the study. Ninety-five (78.5%) out of the 121 had both baseline and follow-up SF-26 scores available. The nurses were mostly baccalaureate prepared [95 (78.5%)] and worked day shift [87 (71.9%)]. The physical health problems subscale at baseline [21.2 (6.0)] was similar to follow-up [22 (6.0); P=0.386]. There was a statistically significant improvement in RAND-36 subscale scores for: physical functioning (P=0.0096), pain (P=0.003), general health perceptions (P<0.0001), energy and fatigue (P<0.0001), social functioning (P=0.0004), emotional health problems (P=0.0422), and emotional well-being (P=0.0348). CONCLUSIONS: This study identified a positive impact on nursing when the nurses self-selected a wellness activity. Positive changes in energy/fatigue, social functioning, and emotional well-being showed potential for healthcare workers and their well-being. Exploring short-term programs that promote autonomous motivation may provide cost-effective wellness programs. Future work should test for sustainability and long-term impact.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".