Digital health education and training for undergraduate and graduate nursing students: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to collate and analyze literature reporting on digital health education and training courses, or other pedagogical interventions, for nursing students at the undergraduate and graduate level to identify gaps and inform the development of future educational interventions. INTRODUCTION: In this era of technology-driven health care, upskilling and/or reskilling the nursing workforce is urgently needed for nurses to lead the digital health future and improve patient care. While informatics competency frameworks serve to inform nursing education and practice, they do not address the entire digital health spectrum. INCLUSION CRITERIA: This review will include research studies, theoretical/discussion papers, and reports, as well as gray literature from relevant sources published in the last 10 years. Opinion pieces, editorials, conference proceedings, and papers published in languages other than English will be excluded. METHODS: The JBI methodology for scoping reviews will be followed. Searches will be conducted in Embase, CINAHL, ERIC, MEDLINE, Scopus, and Education Research Complete to retrieve potentially relevant studies. Hand searches of reference lists of included studies will be conducted. Two reviewers will independently screen records against predefined eligibility criteria and consult a third reviewer if conflicts arise. Decisions will be documented using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram. Quantitative data will be analyzed using descriptive statistics. Content analysis will be applied to qualitative data to identify categories and themes. Findings will be synthesized and reported in tables and narrative format. REVIEW REGISTRATION NUMBER: Open Science Framework osf.io/42eug.
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.
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 | 0.130 | 0.092 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.020 |
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".