Enhancing clinical nursing education for Gen Z students through brain-based learning
Bibliographic record
Abstract
Clinical nursing is the most significant aspect of nursing education. An early exposure to clinical practice can be beneficial for nursing students, fostering a deeper comprehension of real-life nursing care. However, this experience may induce stress among students who might feel underprepared for the significant responsibilities involved. To ensure students attain their utmost potential in acquiring clinical knowledge and skills to deliver high-quality care, it becomes imperative for clinical instructors to critically reflect on their teaching methodologies. Incorporating innovative teaching methods is crucial for engaging students actively and instilling a sense of challenge and motivation during bedside clinical nursing. To involve nursing students actively in clinical learning, instructors need to connect brain neurotransmitters in the quest for learning. Without sufficient stimulation of the brain and its neurotransmitters and hormones during the learning process, students may struggle to grasp and retain knowledge over the long term. This literature review highlights the significance of using the brain-based approach in clinical education to address the needs of Gen Z students. Embracing a brain-based approach can lead to a revolutionary change in nursing education and clinical practice. By associating the brain’s physiology and leveraging advanced learning processes, clinical instructors can adeptly cultivate patient-centered, critical-thinking, practice-ready nurses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".