Factors related to perioperative nurses' job satisfaction and intention to leave
Why this work is in the frame
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Bibliographic record
Abstract
AIM: This study investigated factors associated with perioperative nurses' job satisfaction and their intention to leave. Recruitment and retention of nurses are particularly important in a specialist environment such as the perioperative setting where it is especially difficult to attract and retain nurses due to its unique environment. METHODS: Cross-sectional data were drawn from a larger study on nurses' work environments, conducted in one province of Canada. An e-survey tool, consisting of validated scales, was administered by the provincial nurses' union to a stratified random sample of registered nurses. The study sample consisted of 113 perioperative nurses working in acute-care hospitals. This study included two outcome variables (job satisfaction and intention to leave) and five predictor variables (three aspects of work environment, workload, and emotional exhaustion). Data were analyzed using multivariate linear and logistic regressions. RESULTS: ) of the variance in their intent to leave. After controlling for work status and other predictors, nurse-physician relationship was significantly related to nurses' job satisfaction, and emotional exhaustion was the key predictor for both outcome variables. CONCLUSIONS: This study demonstrated that higher emotional exhaustion is associated with decreased job satisfaction and increased intention to leave among perioperative nurses. The findings suggest that nurse managers should create an empowering and open work environment that fosters perioperative nurses' job satisfaction and reduces their intention to leave.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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