Transitioning to concept-based teaching: A discussion of strategies and the use of Bridges change model
Bibliographic record
Abstract
Nursing education literature is replete with anecdotal accounts of continual struggle with curriculum content saturation. Recent calls, however, for transformation of nursing education have challenged nurse educators to explore innovative pedagogies and consider sweeping changes in the way future nurses are educated. In order to meet the needs of todays health care consumer, nursing education must move away from teacher-centered learning environments to one where students have the primary responsibility and play an active role in their learning. Concept-based teaching (CBT) pedagogies are a novel approach to educating students. Grounded in a constructivist learning theory, CBT allows faculty to build upon students’ prior experiences and acquired knowledge from previous educational endeavors. Concepts that are applicable to multiple care settings are introduced by faculty early in the nursing program and are reinforced with exemplars. The change to CBT is a change in the pedagogical approaches with which faculty are unfamiliar. With student centered learning environments, minimal lecturing, and the increase use of collaborate teamwork, faculty must begin the transition to CBT. The Bridges change model includes three phases that can be used as a framework for identifying strategies to successfully transition to CBT. The use of reflective teaching practice strategies may also enhance the transition to CBT.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".