Experiencing Nursing Governance: Developing A Post Merger Nursing Committee Structure
Bibliographic record
Abstract
In a mid-sized city in south-central Ontario, two hospitals with four physical sites underwent a merger to form one large corporation; this merger was in response to the recommendations of a provincial restructuring commission. Health care delivery within the large corporation was reorganized using a program management structure. An outcome of program management within this corporation was the dissolution of the traditional nursing departments. In recognition of the need for a professional voice, the corporation created a new governance structure, which included the Professional Advisory Committee. Twenty-five disciplines are represented within this committee; each of these disciplines created its own professional committee. Nursing, then, was responsible for developing the Nursing Practice Committee (NPC). The following article describes the process by which front line nursing staff developed the NPC. A nursing structure task force was struck to accomplish this purpose; the task force is described, including membership, mandate, activities, principles and goals. The environmental assessment that was conducted by the task force is described, along with the process by which the NPC structure was designed and implemented. Challenges and successes experienced are presented. Rosabeth Kanter's framework for staff empowerment is used to understand how nursing governance was transformed in the development of the NPC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".