Predictors of new graduate nurses’ workplace well-being
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: New graduate nurses currently experience a stressful transition into the workforce, resulting in high levels of burnout and job turnover in their first year of practice. PURPOSE: This study tested a theoretical model of new graduate nurses' worklife derived from the job demands-resources model to better understand how job demands (workload and bullying), job resources (job control and supportive professional practice environments), and a personal resource (psychological capital) combine to influence new graduate experiences of burnout and work engagement and, ultimately, health and job outcomes. METHODOLOGY/APPROACH: A descriptive correlational design was used to test the hypothesized model in a sample of newly graduated nurses (N = 420) working in acute care hospitals in Ontario, Canada. Data were collected from July to November 2009. Participants were mailed questionnaires to their home address using the Total Design Method to improve response rates. All variables were measured using standardized questionnaires, and structural equation modeling was used to test the model. FINDINGS: The final model fit statistics partially supported the original hypothesized model. In the final model, job demands (workload and bullying) predicted burnout and, subsequently, poor mental health. Job resources (supportive practice environment and control) predicted work engagement and, subsequently, lower turnover intentions. Burnout also was a significant predictor of turnover intent (a crossover effect). Furthermore, personal resources (psychological capital) significantly influenced both burnout and work engagement. PRACTICE IMPLICATIONS: The model suggests that managerial strategies targeted at specific job demands and resources can create workplace environments that promote work engagement and prevent burnout to support the retention and well-being of the new graduate nurse population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it