Implementing a new pedagogy in the nursing curriculum: Bachelor students’ evaluation
Bibliographic record
Abstract
Background: Inspired by the work of Benner and colleagues at Carnegie Foundation, a new course in nursing was implemented in the first study year’s curriculum in the bachelor program in nursing. The new nursing course included a shift from a lecture-only classroom based approach to a problem-based and case-based approach. Reflections and discussions in groups with fellow students and supervisors was the main activity. The aim of this study was to examine how the students experienced the new nursing course.Methods: The survey study was conducted at a university in southern Norwegian. The sample consisted of students in two subsequent classes (n = 126 and n = 118), who had followed the new study plan in the first study year and the traditional study plan in the second study year. An electronic survey, including questions concerning the extent, quality and usefulness of the study plans were examined.Results: The students were, in general, satisfied with the new nursing course regarding the content and quality. The teachers’ presence and ability to engage, challenge and facilitate reflection seemed to be decisive. Reflecting and discussing real-life patient cases in groups with fellow students and a supervisor was stimulating, motivating and useful for learning professional nursing.Conclusions: A problem based, case-based pedagogy might increase the students’ preparedness for solving patient problems that they encounter in clinical settings. Cooperation and reflection in small groups with fellow students and the supervisor enhance nursing students’ clinical reasoning skills, and might contribute to reduce the gap between theory and practice.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".