Factors Associated With Intent to Leave in Registered Nurses Working in Acute Care Hospitals: A Cross-Sectional Study in Ontario, Canada
Bibliographic record
Abstract
Background: The work environment factors associated with nurses’ intention to leave their jobs are not well understood because most studies have used non-probabilistic sampling methods, thus restricting the generalizability of the results. This study examined the relationship between work environment factors and intent to leave among nurses working in acute care hospitals in Ontario, Canada. Methods: This study included a random sample of 1,427 registered nurses who were part of a larger cross-sectional study and who responded to a mailed survey that included measures of resource availability, interprofessional collaboration, job satisfaction, and demographics. Results: Most of the respondents were female (94.8%), with an average age of 45.6 years, and 14.5 years of nursing experience at their current workplace, which included mostly urban (94.6%) and non-teaching hospitals (61.8%). In the multivariate model, we observed that the work environment variables explained 45.5% of the variance in nurses’ intent to leave scores, F(9, 1362) =125.41, p < .01, with an R 2 of .455 or 45.5%. Job satisfaction ( p < .01), flexible interprofessional collaborative relationships ( p = .030), and resource availability ( p < .01) were significantly associated with nurses’ intent to leave scores. Conclusion/Application to Practice: Nurses who reported greater job satisfaction, flexible interprofessional relationships, and resource availability were less likely to express an intent to leave their hospital workplaces. Employers and health policy makers may use these findings as part of a broader strategy to improve the work environment of nurses. Occupational health nurses are ideally positioned to demonstrate leadership in promoting retention efforts in the workplace by advocating for the importance of job satisfaction, flexible interprofessional relationships, and resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".