Education About Digital Health and Artificial Intelligence and Learning Needs: Perspectives of Undergraduate Nursing Students
Bibliographic record
Abstract
Background: Nurses have long used different types of technologies in clinical practice and nursing education. Technology is rapidly evolving, and nurses must keep pace. Purpose: This study aimed to explore nursing students’ perspectives on their preparedness for digital health. Methods: A mixed-methods design was used. Senior-level undergraduate nursing students from two schools of nursing in Eastern and Western Canada participated in focus group interviews and completed a 45-item cross-sectional researcher-developed survey consisting of four parts. We applied in the survey a five-point rating ranging from very low to very high or strongly agree to strongly disagree. Curricular documents were also reviewed to identify how digital health concepts are being incorporated and used to aid in the interpretation of the findings. We analyzed qualitative data using thematic analysis and quantitative data using descriptive statistical analysis. Results: A total of 18 participants took part in focus groups, and 74 completed surveys were included in the analysis. Themes included experiences influencing students’ learning about digital health and suggestions for improving learning about digital health. Survey results were as follows: Self-rated knowledge about digital health and confidence (five items: mean = 17.50/25; SD = 3.30); perceived digital health preparedness and current nursing education opportunities (12 items: mean = 43.65/60; SD = 6.90); perceived benefits and concerns and relevance of digital health to nursing and future practice (22 items: mean = 82.92/110; SD = 6.18); digital health education needs (six items: mean = 23.31/30; SD = 4.59). Findings from both sources corroborate gaps identified in curricular materials. Conclusion: Nursing students have strong digital capabilities and access to some educational opportunities in their schools and in the clinical setting. However, there is a need for more systematic education and improved learning experiences about digital health (existing and emerging technologies such as artificial intelligence) so that the next generation of nurses is better prepared for this era of digital revolution.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".