Factors related to perioperative nurses' job satisfaction and intention to leave
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.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".