Examining the Relationship of Transformational Leadership and New Graduate Nurse Turnover Intention During the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: The nursing shortage in the Canadian healthcare system presents challenges in meeting the healthcare needs of the population and addressing the growing demand for health services. There is strong evidence emphasizing the pivotal role leaders play in fostering retention through positive practices. As new graduate nurses are crucial to the healthcare team, targeted strategies by nursing leaders and managers are needed to enhance nurse retention. Purpose: The purpose of this study was to examine the relationship between transformational leadership and new graduate nurses’ turnover intention, and organizational commitment. Methods: The study included 106 registered nurses who passed the NCLEX-RN between March 2020 and February 2023. Data was collected through the Multifactor Leadership Questionnaire-5X, Turnover Intention Scale-6 and Three-Component Model Employee Commitment Survey. A descriptive correlational design was used to conduct this study with descriptive statistics and Kendall tau-b correlation. Results: A significant negative correlation was found between transformational leadership and turnover intention among new graduate nurses (τb = -.352, p < .001). Conclusion: Nursing leaders must possess the necessary leadership skills rooted in the nursing model to promote transformational leadership and foster an intention to stay among new graduate nurses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".