Challenges and Needs in Digital Health Practice and Nursing Education Curricula: Gap Analysis Study
Bibliographic record
Abstract
Background: Australian nursing programs aim to introduce students to digital health requirements for practice. However, innovation in digital health is more dynamic than education providers' ability to respond. It is uncertain whether what is taught and demonstrated in nursing programs meets the needs and expectations of clinicians with regard to the capability of the nurse graduates. Objective: This study aims to identify gaps in the National Nursing and Midwifery Digital Health Capability Framework , based on the perspectives of clinical nurses, and in nurse educators' confidence and knowledge to teach. The findings will direct a future co-design process. Methods: This study triangulated the findings from 2 studies of the Digital Awareness in Simulated Health project and the National Nursing and Midwifery Digital Capability Framework. The first was a qualitative study that considered the experiences of nurses with digital health technologies during the COVID-19 pandemic, and the second was a survey of nurse educators who identified their confidence and knowledge to teach and demonstrate digital health concepts. Results: The results were categorized by and presented from the perspectives of nurse clinicians, nurse graduates, and nurse educators. Findings were listed against each of the framework capabilities, and omissions from the framework were identified. A series of statements and questions were formulated from the gap analysis to direct a future co-design process with nursing stakeholders to develop a digital health capability curriculum for nurse educators. Conclusions: Further work to evaluate nursing digital health opportunities for nurse educators is indicated by the gaps identified in this study.
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".