Radical transformation: Embracing constructivism and pedagogy for an innovative nursing curriculum
Bibliographic record
Abstract
To effectively navigate today's complex and rapidly changing health care environments, nurses require a high level of knowledge, sound psychomotor skills, diverse thinking and reasoning abilities, and a strong professional identity. The evidence showed that programs that offer students focused clinical practice experiences and offer students opportunities to 'think like a nurse' enable them to become sound practitioners. Faculty and staff at one mid-sized research-intensive university in Western Canada, engaged in an iterative process of rethinking the theoretical and pedagogical underpinnings of a BSN curriculum for educating nurses for the complexity of today's practice. Constructivist learning theory was chosen as the main underpinning of the revised curriculum. Furthermore, transformational learning theory guided the selection of several pedagogical approaches utilized throughout the program with the goal of inviting critical reflection and encouraging the development of competent, compassionate, ethical, and professional nurses. Additionally, we employed an innovative approach to clinical practicums where the process of learning is both integrated and intentional, and students are mentored to use prior knowledge in their decisions and clinical reasoning.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".