Impacts of Technology Use on the Workload of Registered Nurses: A Scoping Review
Bibliographic record
Abstract
Introduction: Technology is an integral part of healthcare. With the rapid development of technological innovations that inform and support nurses, it is important to assess how these technologies may affect their workload particularly in rural contexts, where the workforce and supports may be limited. Methods: This literature review guided by Arksey and O’Malley’s scoping review framework describes the breadth of technologies which impact on nurses’ workload. Five databases (PubMed, CINAHL, PsycInfo, Web of Science, Business Source Complete) were searched. Thirty-five articles met the inclusion criteria. A data matrix was used to organize the findings. Findings: The technology interventions described in the articles covered diverse topics including: Cognitive care technologies; Healthcare providers’ technologies; Communication technologies; E-learning technologies; and Assistive technologies and were categorized as: Digital Information Solutions; Digital Education; Mobile Applications; Virtual Communication; Assistive Devices; and Disease diagnoses groups based on the common features. Conclusion: Technology can play an important role to support nurses working in rural areas, however, not all technologies have the same impact. While some technologies showed evidence to positively impact nursing workload, this was not universal. Technology solutions should be considered on a contextual basis and thought should be given when selecting technologies to support nursing workload.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".