Factors influencing new graduate nurse burnout development, job satisfaction and patient care quality: a time‐lagged study
Bibliographic record
Abstract
AIM: To test a hypothesized model linking new graduate nurses' perceptions of their manager's authentic leadership behaviours to structural empowerment, short-staffing and work-life interference and subsequent burnout, job satisfaction and patient care quality. BACKGROUND: Authentic leadership and structural empowerment have been shown to reduce early career burnout among nurses. Short-staffing and work-life interference are also linked to burnout and may help explain the impact of positive, empowering leadership on burnout, which in turn influences job satisfaction and patient care quality. DESIGN: A time-lagged study of Canadian new graduate nurses was conducted. METHODS: At Time 1, surveys were sent to 3,743 nurses (November 2012-March 2013) and 1,020 were returned (27·3% response rate). At Time 2 (May-July 2014), 406 nurses who responded at Time 1 completed surveys (39·8% response rate). Descriptive analysis was conducted in SPSS. Structural equation modelling in Mplus was used to test the hypothesized model. RESULTS: The hypothesized model was supported. Authentic leadership had a significant positive effect on structural empowerment, which in turn decreased both short-staffing and work-life interference. Short-staffing and work-life imbalance subsequently resulted in nurse burnout, lower job satisfaction and lower patient care quality 1 year later. CONCLUSION: The findings suggest that short-staffing and work-life interference are important factors influencing new graduate nurse burnout. Developing nurse managers' authentic leadership behaviours and working with them to create and sustain empowering work environments may help reduce burnout, increase nurse job satisfaction and improve patient care quality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".