AN EVIDENCED-BASED APPROACH TO REORGANIZING THE NURSING LEADERSHIP STRUCTURE
Bibliographic record
Abstract
OBJECTIVE: The primary aim of the evidence-based practice change was to optimize the nurse manager's span of control by implementing standardized titles and functions. BACKGROUND: Nurse manager roles are critical in healthcare organizations to ensure a high-quality workplace, influence organizational performance, nurse satisfaction and empowerment, staff turnover, and patient outcomes. With the constant changes to healthcare and the complexity, healthcare organizations try to find different ways to reduce costs for day-to-day operations. In response, nurse manager roles may be redesigned or even eliminated, expanding the roles of the remaining nurse managers and increasing their workload and span of control. METHODS: A quality improvement evidence-based practice change was implemented to examine nurse manager span of control in eight inpatient units within a children's hospital in Southwest Florida. Nine newly promoted nurse managers out of 13 participated with a 69% response rate. The Ottawa Hospital span of control tool, nurse manager satisfaction, intent to stay, and time with direct reports were measured for baseline data, weekly post-implementation, and seven weeks post-implementation. RESULTS: Overall, nurse manager satisfaction level and intent to stay decreased seven weeks post-implementation of the new NM role. However, the time spent with direct reports and NM satisfaction level of the time spent with their direct reports increased seven weeks post-implementation from baseline data. CONCLUSION: Healthcare organizations, senior nursing leaders, and nurse managers must continuously evaluate and adjust nurse managers' span of control to maintain appropriate workloads and span of control in the appropriate range that allows nurse managers to function effectively in managing, supporting, and mentoring staff, thus increasing nurse manager job satisfaction and intention to stay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".