Turnover intention of hospital staff in Ontario, Canada: exploring the role of frontline supervisors, teamwork, and mindful organizing
Bibliographic record
Abstract
BACKGROUND: This study contributes to a small but growing body of literature on how context influences employee turnover intention. We examine the impact of staff perceptions of supervisory leadership support for safety, teamwork, and mindful organizing on turnover intention. Interaction effects of safety-specific constructs on turnover intention are also examined. METHODS: Cross-sectional survey data were collected from nurses, allied health professionals, and unit clerks working 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 teamwork were significantly associated with turnover intention (p < 0.001). Direct associations of supervisory leadership support for safety and mindful organizing with turnover intention were non-significant; however, when staff perceived lower levels of mindful organizing at the frontlines, the positive effect of supervisory leadership on turnover intention was significant (p < 0.01). CONCLUSIONS: Our results suggest that, in addition to teamwork perceptions positively affecting turnover intentions, safety-conscious supportive supervisors can help alleviate the negative impact of poor mindful organizing on frontline staff turnover intention. Healthcare organizations should recruit and retain individuals in supervisory roles who prioritize safety and possess adequate relational competencies. They should further dedicate resources to build and strengthen the relational capacities of their supervisory leadership. Moreover, it is important to provide on-site workshops on topics (e.g., conflict management) that can improve the quality of teamwork and consequently reduce employees' intention to leave their unit/organization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".