The roles of unit leadership and nurse–physician collaboration on nursing turnover intention
Bibliographic record
Abstract
AIM: To report a study of the relationship between variables at the group and individual level with nurses' intention to leave their unit. BACKGROUND: Workplaces are collective environments where workers constantly interact with each other. The quality of working relationship employees develop at the unit-level influences both employee outcomes and unit performance by shaping employee attitudes. DESIGN: The study was a cross-sectional design with self-administered questionnaires. METHODS: A questionnaire including measures of leader-member exchange and nurse-physician collaboration analysed at group-level and affective commitment and turnover intention analysed at individual level, was administered individually to 1018 nurses in five Italian hospitals. Data were collected in 2009. RESULTS: A total of 832 nurses (81·7% response rate) completed questionnaires. The results showed that affective commitment at individual level completely mediated the relationship between leader-member exchange at group-level and nursing turnover intention. Furthermore, the cross-level interaction was significant: at individual level, the nurses with high levels of individual affective commitment towards their unit showed low levels of turnover intention and this relationship was stronger when the nurse-physician collaboration at group-level was high. CONCLUSION: This study showed the importance for organizations to implement management practices that promote both high-quality nurse-supervisor and nurse-physician relationships, because they increase nurses' identification with their units. Individual affective commitment is an important quality for retaining a workforce and good nurses' relationship at group-level relationships with both supervisors and physicians are instrumental in developing identification with the work unit. Thus, the quality of relationship among staff members is an important factor in nurses' decision to leave.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".