Acute care nurses’ perceptions of leadership, teamwork, turnover intention and patient safety – a mixed methods study
Bibliographic record
Abstract
BACKGROUND: This study contributes to a small but growing body of literature on how context influences perceptions of patient safety in healthcare settings. We examine the impact of senior leadership support for safety, supervisory leadership support for safety, teamwork, and turnover intention on overall patient safety grade. Interaction effects of predictors on perceptions of patient safety are also examined. METHODS: In this mixed methods study, cross-sectional survey data (N = 185) were collected from nurses and non-physician healthcare professionals. Semi-structured interview data (N = 15) were collected from nurses. The study participants worked in intensive care, general medicine, mental health, or the emergency department of a large community hospital in Southern Ontario. RESULTS: Hierarchical regression analyses showed that staff perceptions of senior leadership (p < 0.001), teamwork (p < 0.01), and turnover intention (p < 0.01) were significantly associated with overall patient safety grade. The interactive effect of teamwork and turnover intention on overall patient safety grade was also found to be significant (p < 0.05). The qualitative findings corroborated the survey results but also helped expand the characteristics of the study's key concepts (e.g., teamwork within and across professional boundaries) and why certain statistical relationships were found to be non-significant (e.g., nurse interviewees perceived the safety specific responsibilities of frontline supervisors much more broadly compared to the narrower conceptualization of the construct in the survey). CONCLUSIONS: The results of the current study suggest that senior leadership, teamwork, and turnover intention significantly impact nursing staff perceptions of patient safety. Leadership is a modifiable contextual factor and resources should be dedicated to strengthen relational competencies of healthcare leaders. Healthcare organizations must also proactively foster inter and intra-professional collaboration by providing teamwork educational workshops or other on-site learning opportunities (e.g., simulation training). Healthcare organizations would benefit by considering the interactive effect of contextual factors as another lever for patient safety improvement, e.g., lowering staff turnover intentions would maximize the positive impact of teamwork improvement initiatives on patient safety.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".