Leveraging Clinical Preceptorship to Enhance Nursing Students’ Readiness in Digital Health
Bibliographic record
Abstract
As technology continues to advance rapidly and digitalization becomes more prevalent in health care, the nursing profession must also adapt to these changes. The integration of informatics in nursing education and practice should serve as a catalyst for shaping future nursing leaders who can drive innovation, influence policy, and contribute to the advancement of health care. During the final stage of their undergraduate education, nursing students would have gained substantive knowledge and have been exposed to different digital health technologies and medical devices used in the delivery of clinical care. However, their ability to assimilate this knowledge and make sense of how nursing informatics and digital health relate to their practice roles may not be as readily visible to them. Nursing preceptors can play a vital role in assisting students discover the potential of technology in health care and nursing practice. Yet, despite the significance of clinical learning experiences and the important role nursing preceptors play in the development of nursing students, there is limited discussion in the literature with respect to their role in relation to digital health readiness among nursing students. The purpose of this discussion paper is to illuminate the importance of nursing informatics as a foundational knowledge base for Canadian nurses and argue the need for advancing clinical nursing education, particularly preceptorship experiences, as a potential pathway for enhancing nursing students’ readiness in digital health and to facilitate their transition into the registered nurse role in digitally enabled work environments.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".