Creation of a remote clinical practice curriculum for first year nursing students: Reflections and lessons learned
Bibliographic record
Abstract
Introduction: A problem-based learning approach using intentionally created client profiles and nursing care summaries formed the foundation of a remote clinical experience for first year undergraduate nursing students. Methods: Using carefully designed client scenarios, care summaries and active learning strategies, students were introduced to the nursing profession and provided the opportunity to develop skills in collaboration, critical thinking, clinical reasoning, and clinical judgement. Results: Based on discussion with students, the remote experience assisted in the development of several skills addressing communication, theoretical foundation, and critical thinking. As well, this experience allowed for the integration and application of newly acquired nursing knowledge, and an enhanced understanding of the role of the professional nurse. Implications: Significant lessons learned may serve other nursing programs around the world as we continue to navigate both current and future public health mandates while managing competing demands for in-person clinical practice sites. Conclusion: Face-to-face clinical experiences remain a critical component of comprehensive nursing education, however, given today’s climate and continued restrictions, a hybrid model, utilizing a remote platform, is worthy of further exploration.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".