Acceptance of Digital Technology Among Nursing Staff in Geriatric Long-Term Care: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Digital technologies are increasingly being introduced into the healthcare system and in settings like hospitals and geriatric long-term care (LTC) facilities, offering potential benefits such as improved care quality, reduced workload or enhanced documentation processes. However, the success of these technologies depends also on the acceptance by the primary users, the nursing staff. OBJECTIVE: This review synthesizes empirical studies that have explored the acceptance of digital technologies by nursing staff in geriatric LTC settings, building upon the foundational work by Yu et al. (2009). The goal is to identify influencing factors, assess the extent of existing evidence and highlight research gaps in this care setting. METHODS: A systematic literature review was conducted following PRISMA 2020 guidelines. The SPIDER framework was used for eligibility criteria. Databases searched included PubMed, ACM Digital Library, Web of Science and the Health Administration Database ProQuest. Studies were included if they empirically examined the acceptance of digital technologies by nursing staff in geriatric LTC settings. Two reviewers independently screened the studies, extracted data and assessed methodological quality using the CASP (Critical Appraisal Skills Programme) checklist. RESULTS: A total of three studies met the criteria, highlighting a gap in research on this topic. The studies applied cross-sectional quantitative designs, highlighted critical determinants of technology acceptance, including perceived usefulness, ease of use, digital competence and organizational support. The studies involved a total of n=1,019 participants from Germany, Australia and The Netherlands. Barriers included lack of user involvement, lack of training, poor system design and demographic differences in digital affinity. CONCLUSIONS: This review shows that the acceptance of digital technologies by nursing staff in geriatric LTC settings is shaped by a constellation of individual factors, such as digital competence and perceived relevance of technology, as well as organizational factors like access to training and involvement of staff in the implementation process. Despite these insights, the limited number of empirical studies highlights a research gap in this care setting. To ensure sustainable digital transformation in geriatric LTC, future research should prioritize rigorous and participatory approaches, using longitudinal, intervention-based or multilevel study designs.
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".