Job Satisfaction and Retention Survey for Nurses in an Acute Care Hospital in Ontario
Bibliographic record
Abstract
Introduction: The nursing shortage in Ontario persists in a post-pandemic era and poses a challenge in meeting the human resource needs of acute care hospitals. Strong evidence emphasizes the need to understand what satisfies and retains registered nurses (RNs) and registered practical nurses (RPNs) working in Ontario hospitals. Limited quantitative studies have examined job satisfaction and retention of RNs and RPNs in Ontario (Canada). Purpose: The purpose of this study was to adapt and develop a valid and reliable instrument to evaluate the job satisfaction and retention intent of RNs and RPNs working in acute care settings in Southwestern Ontario. Methods: A pilot study of 88 RNs and RPNs employed across three acute care hospitals within the Niagara Health System completed surveys via Qualtrics; data collection occurred between July 2024 and September 2024. The dataset was analyzed using descriptive statistics, bivariate, multivariate, and thematic analysis. Results: Overall, participants' job satisfaction and retention scores were high, with RPNs demonstrating statistically significantly higher job satisfaction than RNs. Themes included hiring more staff, improving compensation, fostering a positive work environment, and enhancing management-staff communication. Conclusion: Our study used an adapted and validated survey to examine job satisfaction and retention among RNs and RPNs in three acute-care hospitals in Ontario (Canada). The hallmark results demonstrated that RNs and RPNs reported high job satisfaction and retention levels. With further psychometric testing of this study’s survey, the newly developed survey could be used to study other healthcare settings across Ontario and Canada.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".