Effects of workplace incivility and empowerment on newly-graduated nurses’ organizational commitment
Bibliographic record
Abstract
AIM: The purpose of the present study was to test an expanded model of Kanter's theory by examining the influence of structural empowerment, psychological empowerment and workplace incivility on the organizational commitment of newly-graduated nurses. BACKGROUND: The first years of practise represent an important confidence-building phase for newly-graduated nurses, yet many new nurses are exposed to disempowering experiences and incivility in the workplace. METHOD: A predictive non-experimental design was used to examine the impact of structural empowerment, psychological empowerment and workplace incivility on the affective commitment of newly-graduated nurses (n=117) working in acute care hospitals. RESULTS: Controlling for age, 23.1% of the variance in affective commitment was explained by structural empowerment, psychological empowerment and workplace incivility [R²=0.231, F(5,107) =6.43, P=0.000]. Access to opportunity was the most empowering factor, with access to support and formal power perceived as least empowering. Perceived co-worker incivility was greater than perceived supervisor incivility. CONCLUSION: Results offer significant support for the use of Kanter's theory in the newly-graduated nurse population. IMPLICATIONS FOR NURSING MANAGEMENT: Without specific strategies in place to combat incivility and disempowerment in the workplace, attempts to prevent further organizational attrition of new members may be futile.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".