Mitigating Turnover in Healthcare: Exploring Servant Leadership and Psychological Capital
Bibliographic record
Abstract
In the aftermath of the COVID-19 pandemic, understanding factors that influence the turnover intentions of healthcare workers is critical. This quantitative correlational study explored how psychological capital mediates the relationship between perceived supervisor servant leadership and turnover intentions among Canadian healthcare workers. This study uses the Hayes PROCESS Model 4 for mediation and Pearson’s correlation analysis with participants recruited via social media. Out of 773 respondents, 648 valid responses were received (83.8%), of which the majority were aged between 24-34 years old (74.7%), were nurses (80.2%), and had tenure between six to ten years (75%). In agreement with prior literature, the findings revealed that servant leadership indirectly affected turnover intentions by raising psychological capital. This study adds to the body of knowledge, as no other study integrated these variables in the context of Canadian health workers (doctors and nurses). Practical implications are for hospitals to invest in training for servant leadership and psychological capital to help retain employees.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".