Case Study: Leading Change across Two Sites: Introduction of a New Documentation System
Bibliographic record
Abstract
Leading change initiatives across multiple geographic locations has become increasingly frequent among nursing leaders as hospitals merge to form more effective and powerful organizations.This case study illustrates how strong nursing leadership, in conjunction with a transformational change approach, led to the successful implementation of a new documentation system at two hospitals within one organization.The project process is described in detail, including a discussion of lessons learned, and recommendations are provided for the leadership of future multi-site projects.Hospital mergers are on the rise in Canada (Markham and Lomas 1995) and so, therefore, is the need to lead across multiple geographic locations.This form of restructuring is accompanied by other complex and rapid changes in healthcare (Taccetta-Chapnick 1996).Because of the need to manage change in such a challenging environment, the Canadian Nurses Association (2005) has called for greater nursing leadership.MacLaren (2002), a director of transitional services in Southern California, claimed that the most common reason to lead or manage across more than one hospital site is a merger.Jordan and Stuart (2000), both high-profile consultants working in the healthcare sector, provided several reasons why two or more hospitals may merge, INNOVATION IN LEADERSHIP
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".