New graduate nurses’ experiences of bullying and burnout in hospital settings
Bibliographic record
Abstract
AIM: This paper is a report of a study conducted to test a model linking new graduate nurses' perceptions of structural empowerment to their experiences of workplace bullying and burnout in Canadian hospital work settings using Kanter's work empowerment theory. BACKGROUND: There are numerous anecdotal reports of bullying of new graduates in healthcare settings, which is linked to serious health effects and negative organizational effects. METHODS: We tested the model using data from the first wave of a 2009 longitudinal study of 415 newly graduated nurses (<3 years of experience) in acute care hospitals across Ontario, Canada. Variables were measured using the Conditions of Work Effectiveness Questionnaire, Negative Acts Questionnaire-Revised and Maslach Burnout Inventory-General Survey. RESULTS: The final model fit statistics revealed a reasonably adequate fit (χ² = 14·9, d.f. = 37, IFI = 0·98, CFI = 0·98, RMSEA = 0·09). Structural empowerment was statistically significantly and negatively related to workplace bullying exposure (β = -0·37), which in turn, was statistically significantly related to all three components of burnout (Emotional exhaustion: β = 0·41, Cynicism: β = 0·28, EFFICACY: β = -0·17). Emotional exhaustion had a direct effect on cynicism (β = 0·51), which in turn, had a direct effect on efficacy (β = -0·34). Conclusion. The results suggest that new graduate nurses' exposure to bullying may be less when their work environments provide access to empowering work structures, and that these conditions promote nurses' health and wellbeing.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".