Work Experience and Anger Management in Nurses: Cross-Sectional Analysis Based on Benner’s Novice to Expert Theory
Bibliographic record
Abstract
Background: Nursing is an emotionally demanding profession where unmanaged anger can compromise patient care and teamwork. While clinical experience is thought to enhance emotional regulation, the relationship between work experience and anger management remains poorly understood. Objective: This study aimed to assess whether work experience predicts anger management ability among nurses, using Benner's Novice to Expert Theory as a guiding framework. Methods: A descriptive cross-sectional study was conducted in 2024 involving 265 nurses working in hospitals affiliated with Kermanshah University of Medical Sciences, Kermanshah, Iran. Stratified random sampling was used based on hospital wards. Data were collected using a demographic questionnaire and the State-Trait Anger Expression Inventory-2. Statistical analyses included Pearson correlation analysis, t tests, ANOVA, and multiple linear regression analysis. Normality was tested using the Kolmogorov-Smirnov test. The sample size was determined using parameters referenced in prior studies and confirmed with G*Power software (Heinrich-Heine-University Düsseldorf). Results: Although nurses with more experience reported slightly higher anger control scores, the correlation between work experience and anger management was not significant (r=-0.079, P=.18). Regression analysis revealed that shift type and job security significantly predicted anger regulation, independent of experience level. Conclusions: Work experience alone does not ensure improved anger management among nurses. Organizational factors such as shift scheduling and employment stability may have a greater influence on emotional regulation. Institutions are encouraged to provide structured support and stress management training, especially for early-career nurses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".