The impact of emotional intelligent leadership on staff nurse empowerment: the moderating effect of span of control
Bibliographic record
Abstract
AIM: To test a model linking nurses' perceptions of their nurse manager's emotionally intelligent leadership style and nurses' structural empowerment, and the impact of nurse manager span of control (number of direct reports) on the emotional intelligence/empowerment relationship. BACKGROUND: Hospital restructuring in the 1990s resulted in a dramatic reduction in nurse manager positions, yet nurse managers are critical to empowering nurses for professional practice. METHOD(S): A descriptive correlational survey design was used to test the hypothesized model in two community hospitals in Ontario. Two hundred and three nurses from two hospitals returned useable questionnaires (68% response rate). RESULTS: Span of control was a significant moderator of the relationship between nurses perceptions of their managers' emotionally intelligent behaviour and feelings of workplace empowerment. CONCLUSION: The results suggest that even managers with strong emotional intelligence may not be able to empower their staff if their span of control is large. IMPLICATIONS FOR NURSING MANAGEMENT: Every effort must be made to ensure that managers have reasonable spans of control that allow them to develop and use the leadership skill necessary for empowering their staff to practice to the full scope of their professional role.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".