Sociotechnical Challenges of Digital Health in Nursing Practice During the COVID-19 Pandemic: National Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has accelerated the use of digital health innovations, which has greatly impacted nursing practice. However, little is known about the use of digital health services by nurses and how this has changed during the pandemic. OBJECTIVE: This study explored the sociotechnical challenges that nurses encountered in using digital health services implemented during the pandemic and, accordingly, what digital health capabilities they expect from the emerging workforce. METHODS: Five groups of nurses, including chief nursing information officers, nurses, clinical educators, nurse representatives at digital health vendor companies, and nurse representatives in government bodies across Australia were interviewed. They were asked about their experience of digital health during the pandemic, their sociotechnical challenges, and their expectations of the digital health capabilities of emerging nurses to overcome these challenges. Interviews were deductively analyzed based on 8 sociotechnical themes, including technical challenges, nurse-technology interaction, clinical content management, training and human resources, communication and workflow, internal policies and guidelines, external factors, and effectiveness assessment of digital health for postpandemic use. RESULTS: Sixteen participants were interviewed. Human factors and clinical workflow challenges were highly mentioned. Nurses' lack of knowledge and involvement in digital health implementation and evaluation led to inefficient use of these technologies during the pandemic. They expected the emerging workforce to be digitally literate and actively engaged in digital health interventions beyond documentation, such as data analytics and decision-making. CONCLUSIONS: Nurses should be involved in digital health interventions to efficiently use these technologies and provide safe and quality care. Collaborative efforts among policy makers, vendors, and clinical and academic industries can leverage digital health capabilities in the nursing workforce.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".