The Impact of Digital Health Transformation Driven by COVID-19 on Nursing Practice: Systematic Literature Review
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has accelerated the uptake of digital health innovations due to the availability of various technologies and the urgent health care need for treatment and prevention. Although numerous studies have investigated digital health adoption and the associated challenges and strategies during the pandemic, there is a lack of evidence on the impact on the nursing workforce. OBJECTIVE: This study aims to identify the impact of digital health transformation driven by COVID-19 on nurses. METHODS: The online software Covidence was used to follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Relevant scientific health and computing databases were searched for papers published from January 2020 to November 2021. Using the 8D sociotechnical approach for digital health in health care systems, the papers were analyzed to identify gaps in applying digital health in nursing practice. RESULTS: In total, 21 papers were selected for content analysis. The analysis identified a paucity of research that quantifies the impact of the digital health transformation on nurses during the pandemic. Most of the initiatives were teleconsultation, followed by tele-intensive care unit (tele-ICU), and only 1 (5%) study explored electronic medical record (EMR) systems. Among the sociotechnical elements, the human-related factor was the most explored and the system measurement was the least studied item. CONCLUSIONS: The review identified a significant gap in research on how implementing digital health solutions has impacted nurses during the COVID-19 pandemic. This gap needs to be addressed by further research to provide strategies for empowering the nursing workforce to be actively involved in digital health design, development, implementation, use, and evaluation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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