Staff Nurse Empowerment in Line and Staff Organizational Structures for Chief Nurse Executives
Bibliographic record
Abstract
OBJECTIVE: The authors tested a model linking chief nurse executive (CNE) organizational structures (line and staff) to staff nurse perceptions of workplace empowerment in 2 large Canadian hospitals. BACKGROUND: Kanter's theoretical constructs of empowerment (ie, access to information, support, resources and opportunity, and formal and informal power) were used to explore this phenomena. No published studies were found linking organizational structure to staff nurse empowerment. METHODS: Staff nurses (n = 256) were surveyed in 2 large teaching hospitals, one with a CNE in a line structure, the other with a CNE in a staff structure. Multiple regression analysis was used to test the proposed model. RESULTS: Staff nurses with a CNE in a line structure felt significantly more empowered in their access to resources than nurses with a CNE in a staff structure. Kanter's empowerment structures explained 63% of the variance in nurses' global empowerment in a line structure and 42% in the staff structure. Access to information, resources, and formal power was an important predictor of nurses' global empowerment in the line hospital, whereas only access to support was a significant predictor in the staff hospital. CONCLUSION: Support for the model tested in this study highlights the importance of the CNE in creating and sustaining healthy work environments for nurses.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".