A Stress Relief App Intervention for Newly Employed Nursing Staff: Quasi-Experimental Design
Bibliographic record
Abstract
BACKGROUND: Most newly employed nurses have limited practical experience, lack problem-solving abilities, and have low resistance to stress, and therefore often opt to resign from the nursing profession. OBJECTIVE: This study aimed to assess the effectiveness of a stress relief app (SR_APP) to monitor the stress levels of newly employed nurses. METHODS: We conducted a quasi-experiment to assess changes in stress levels of newly employed nurses at a case hospital, in which the experimental group used the SR_APP and the control group did not. In-depth interviews were conducted to reveal insights regarding their stress. The app usage experiences of experimental group members were assessed via a questionnaire. RESULTS: All the participants appreciated the experiment and were interested to know more about managing their stress. The experimental group members showed significant differences in heart rate variability scores before and after using the SR_APP, and they reported high levels of intention to use and satisfaction with regard to the SR_APP. CONCLUSIONS: The SR_APP can be effective in helping newly employed nurses to manage their stress.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".