Nurse Managers’ Leadership Style and Retention of Registered Nurses in Canada
Bibliographic record
Abstract
Registered nurse shortages and high turnover rates are a problem in Canada. Few studies have been conducted that concentrate on nurse managers’ leadership styles in relation to retaining experienced registered nurses in Canada. This qualitative study was conducted to examine nurse managers’ leadership styles in relation to Canada’s declining retention of registered nurses. Leadership-motivated theory was used as a theoretical framework. Data were collected through semistructured interviews conducted with five registered nurses and three nurse managers, who all had a minimum of 5 years of experience in their respective roles in public healthcare centers in the province of Alberta, British Columbia, and Ontario. Data and documents were also obtained from organizational websites for triangulation of data. Data were analyzed using computer-assisted qualitative data analysis software. Analysis of the data led to the identification of three main themes and three subthemes regarding nurse managers’ leadership styles and registered nurse retention in Canada. Main themes were (a) job satisfaction, (b) retention strategies for registered nurses, and (c) nurse management assistance. Subthemes were (a) compensation and wage increase, (b) facilitating access to continuous education, and (d) appreciation. The findings of this study have potential implications for positive social change by providing health care centers with leadership strategies that could lower registered nurses turnover rate, boost retention, improve patient care, enhance registered nurses’ job satisfaction, and address the shortage of registered nurses in 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 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".