Nursing faculty and student transition to a context–based learning curriculum
Bibliographic record
Abstract
Background: Context-based learning (CBL) a student-centered teaching and learning approach (STL) is used at the study site to teach undergraduate nursing theory. While it is widely accepted that transition to STL can be difficult, little is known about the perception of nursing faculty and students transition to CBL. The purpose of this study was to describe nursing faculty and students’ perception about their transition to CBL.Methods: An exploratory-descriptive qualitative design was undertaken to understand and describe the perception of nursing faculty and students transition to CBL as a teaching and learning strategy for undergraduate nursing theory courses.Results: Five themes emerged from the students’ data: throwing someone into an ocean, sink or swim, turning point, just doing it, and valuing. Four themes emerged from the faculty data on transition to CBL: an adaptation, trusting the CBL process, a maturing process for students, and controversies about CBL. While the transition was uncomfortable for students and faculty, over time both understood the benefits of CBL.Conclusions: Nursing as a practice discipline will best be served using a variety of teaching and learning strategies in the undergraduate education of nursing students. Based on the findings of this study, recommendations have been made to smooth the transition for faculty members and students not previously exposed to the STL approach using CBL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".