A Faculty Created Strategic Plan for Excellence in Nursing Education
Bibliographic record
Abstract
Strategic planning for nursing education, when seen through a faculty lens creates a deeper, more meaningful critical analysis of effective program development. New strategies are required for academic institutions to transform their curricula to meet the needs of a dynamic healthcare and changing global environment to provide quality education for students. In this article, an evidence-informed process is presented that was progressively co-created by the faculty and facilitators. Seminal business frameworks, leadership development philosophies, and innovative interventions enabled faculty to become engaged and developed as they created a strategic plan for a future-driven nursing program. Phase One presents the process of developing a strategic plan for excellence in nursing education by leveraging faculty potential and preparing for an upcoming accreditation. In Phase Two, four team members from Phase One continue as part of Phase Two team serving as the collective memory for this initial work. This method of strategic planning encouraged faculty engagement and leadership and laid the groundwork for a positive culture change among nursing faculty.
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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 | 0.029 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".