First-line nurse leaders’ health-care change management initiatives
Bibliographic record
Abstract
AIM: To examine nurse leaders' change management projects within British Columbia, Canada. BACKGROUND: British Columbia Nursing Leadership Institute 2007-10 attendees worked on year-long change management initiatives/projects of importance to their respective health-care institutions. Most leaders were in first-line positions with <3 years' experience. METHOD: Consenting leaders' project reports (N = 133) were content analysed for specific themes: types of projects; scope of projects (e.g. unit or local level, departmental, institutional); influence targets or key stakeholder groups targeted by the projects; leadership successes and challenges. RESULTS: Of study participants, 77% successfully completed their projects. Staff tool and resource development and existing services improvement were major project types. Care delivery teams were the major influence targets. Only 25% of projects were at the unit level. Many projects had broader scopes, such as institutional levels. Participants cited multiple leadership successes, including enhanced leadership styles and organizational skills. CONCLUSION: First-line nurse leaders were able to successfully manage projects beyond their traditional scope of responsibilities. The majority of projects dealt with staff needs and healthcare restructuring initiatives. IMPLICATIONS FOR NURSING MANAGEMENT: Constant change is a global reality. Change management, a universal competency, must be included in leadership development programmes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".