Transformational and abusive leadership practices: impacts on novice nurses, quality of care and intention to leave
Bibliographic record
Abstract
AIMS: To investigate the impact of nurse managers exercising transformational vs. abusive leadership practices with novice nurses. BACKGROUND: In a nursing shortage context, it is important to understand better the factors that potentially influence the retention of nurses in the early stages of their career. A large body of research has found that transformational leadership practices have a positive influence on employee functioning. However, very little research exists about the detrimental impact of abusive leadership practices, much less in a nursing context. DESIGN: A cross-sectional design where 541 nurses from the province of Quebec (Canada) were questioned in the fall of 2013. METHODS: A self-administered questionnaire was completed by nurses with less than five years of nursing experience. RESULTS: Results from three linear regression analysis indicated that transformational leadership practices potentially lead to high quality care and weak intention to quit the healthcare facilities. Conversely, abusive leadership practices potentially lead to poorer quality care and to strong intention to quit the healthcare facilities and the nursing profession. CONCLUSION: Paying close attention to the leadership practices of nurse managers could prove effective in improving patient care and increasing the retention of new nurses, which is helpful in resolving the nursing shortage. Our results specifically suggest not only that we promote supportive leadership practices (transformational leadership) but, most of all, that we spread the word that abusive leadership creates working conditions that could be detrimental to the practice of nursing at career start.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".