Are Future Nurses Ready for Digital Health?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Research continues to show significant gaps in nursing graduates' preparedness in digital health. PURPOSE: The aim of this study was to explore nursing students' self-perceived nursing informatics competency and preparedness in digital health, describe learning opportunities available, and identify perceived learning barriers and facilitators to developing informatics competency. METHODS: A sequential mixed-methods design, using a cross-sectional survey and interviews, was used. Senior undergraduate students (n = 221) in BScN programs in a Western Canadian Province participated. RESULTS: Participants self-reported being somewhat competent in nursing informatics. Three themes were identified: struggling to make sense of informatics nursing practice; learning experiences; and preparedness for future practice. CONCLUSION: Addressing inconsistencies in informatics education is an urgent priority so that nursing graduates are competent upon joining the workforce. Implications for nursing education, practice, and policy are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it