Renewal of an entry to practice baccalaureate nursing curriculum: Adapting to complexity
Bibliographic record
Abstract
Curriculum re-design in entry to practice nursing degrees requires a rigorous and multifaceted approach to align the needs of students, professional and industry stakeholders, community needs, the faculty’s vision and university and regulator requirements. This paper relates the initial steps in the process taken to achieve this re-design in one Australian university’s Bachelor of Nursing program, and describes our experiences in two parts. The first part outlines the context in which the need for curriculum renewal was triggered and the ensuing processes undertaken in the development of our new course aim, course outcomes and graduate attributes. The second part discusses how undertaking these activities then came to influence the adoption of Complexity Thinking in the design of our conceptual model, which then guided our program structure and overarching learning and teaching approaches. We share these experiences to illustrate the steps we undertook on this journey, to outline and example the program we created, and to continue the scholarly discussions around the design of baccalaureate nursing program structures, especially those that implement pedagogies inspired by the concepts related to Complexity Theory. The choice of complexity thinking as a guiding theory was key in providing the lens through which we were inspired to graduate nurses with the skills to provide care in complex situations and value the learning that comes through uncertainty, reflection, adaptation and emergence.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".