The influence of areas of worklife fit and work-life interference on burnout and turnover intentions among new graduate nurses
Bibliographic record
Abstract
AIM: To examine the relationships among the overall person-job match in the six areas of worklife, work-life interference, new nurses' experiences of burnout and intentions to leave their jobs. BACKGROUND: As a large cohort of nurses approaches retirement, it is important to understand the aspects of the nurses work-life that are related to turnover among new graduate nurses to address the nursing workforce shortage. METHODS: Secondary analysis of data collected in a cross-sectional survey of 215 registered nurses working in Ontario acute hospitals was conducted using structural equation modelling. RESULTS: The fit indices suggested a reasonably adequate fit of the data to the hypothesised model [χ(2) = 247, d.f. = 122, P = 0.001, χ(2) /d.f. = 2.32, Incremental Fit Index (IFI) = 0.954, Comparative Fit Index (CFI) = 0.953, Root Mean Square Error of Approximation (RMSEA) = 0.06]. Person-job match in six areas of worklife had a direct negative effect on burnout (emotional exhaustion and cynicism), which in turn had a direct positive effect on turnover intentions. Work-life interference also influenced turnover intentions indirectly through burnout. CONCLUSION: The study findings demonstrate that new graduate nurses' turnover intentions are a recurring problem, which could be reduced by improving nurses' working conditions. Retention of new graduate nurses could be enhanced by creating supportive working environments to reduce the susceptibility to workplace burnout, and ultimately, lower turnover intentions. IMPLICATIONS FOR NURSING MANAGEMENT: Managers must employ strategies to enhance workplace conditions that promote a person-job fit and work-life balance to improve retention of new graduate nurses, and, thereby, lessen the nursing shortage.
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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 | 0.001 | 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 | 0.001 | 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".