Bibliographic record
Abstract
Objective: Previous studies had found that rest breaks can not only prevent or improve negative reactions to stress in healthcare staff, but also reduce turnover in understaff conditions, improve work performance, and ultimately improve patient outcomes. As a result, many inpatient units had implemented a nursing position called a Break Nurse, whose sole purpose was to provide rest breaks. However, the implementation of a Break Nurse and its effectiveness was not reported in literature. Therefore, this quality improvement QI project proposed to implement Break Nurses on an acute care unit of impatient setting and investigate its effectiveness on nursing staff.Methods: The selected unit previously utilizing a Break-Buddy model for securing rest breaks was able to start a two 8-hour shift Break Nurse model. The hypothesis is that the Two-Break-Nurse model, when compared with the Break-Buddy model, will better secure rest breaks, reduce burnout symptoms experienced by nursing staff. The validated tool used to measure burnout is the Maslach Burnout Inventory. The study utilized pre- and post-implementation self-report survey statistical analysis to report outcomes.Results: In the end, 14 individuals had responded to both pre- and post implementation surveys. The results show that there was statistically significant improvement of Emotional Exhaustion. Due to the small sample size, the measurement of Depersonalization and Personal Accomplishment did not show statistically significant improvement.Conclusions: The Two-Break-Nurse model is effective at reducing emotional exhaustion for nursing staff. Further studies are needed to measure in a larger scale the effectiveness of break nurse model on other aspects of burnout and the improvement of clinical 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".