Cultural Shift From a Teacher- to a Learner-Centered Teaching Environment in an Undergraduate Nursing Program
Bibliographic record
Abstract
The changing Canadian demography—resulting in an older population with increasing health issues—coupled with the ongoing pandemic, requires nursing programs to produce graduates ready to provide safe, competent nursing care. Learner-centered teaching (LCT) is an effective approach that facilitates deep learning and the emergence of a new meaning of knowledge. However, teacher centeredness in nursing education that emphasizes memorized knowledge versus higher-order thinking is still prevalent. This Organizational Improvement Plan (OIP) aims to shift the beliefs, attitudes, and practices of the nursing faculty in an undergraduate nursing program in Ontario, Canada, from those of a more traditional lecturer role to those of a learning co-facilitator. The results of recent program exit surveys completed by graduating students support the need for such intervention, showing that the current teaching practice is misaligned with the philosophy of the nursing school and the program. The OIP is approached from an interpretivist paradigm and cultural lens, utilizing the shared, transformational, and adaptive leadership frameworks, all of which underscore the experiences of the teachers and learners as they co-construct reality, while emphasizing that multiple meanings may exist across individuals and groups. In alignment with these theoretical frames, the stages of the cycle-of-learning-and-change framework and the change path model were selected to implement change. Thus, these frameworks serve as guides for the detailed implementation, monitoring and evaluation, and communication plans. Future considerations include the expansion of the change project to other programs in the nursing school and the scientific evaluation of LCT in the nursing program.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".