Assessing the Use of Welfare Technology in Social Care for Older Adults Through Assistant Nurses’ Perceptions of Upskilling and Care Delivery Outcomes: Cross-Sectional Study
Bibliographic record
Abstract
Background: The implementation of welfare technologies, a broad array of technologies that have the potential to maintain or improve individuals' safety, independence, and participation, has increased rapidly in recent years, offering new ways of delivering care. However, studies of welfare technology use in the social care sector are scarce. Objective: This study aims to explore the use of different types of welfare technologies, training in the use of these technologies, and to identify their associations with outcomes for care recipients and frontline care workers in the social care of older adults. Methods: A cross-sectional survey was conducted based on a nationwide randomized sample of assistant nurses employed in social care for older adults in Sweden (N=1163; response rate 23%). Dependent variables were outcomes for care recipients (continuity of care, participation, and reduction in loneliness) and upskilling for frontline care workers. The exposure variables were types of welfare technologies and training in the use of such technologies. Associations between exposure and dependent variables were assessed through logistic regression models. Results: According to the perceptions of care workers, interactive technologies were significantly positively associated with continuity of care (odds ratio [OR] 1.58, 95% CI 1.15-2.18), participation (OR 2.01, 95% CI 1.48-2.74), and reduction in loneliness among care recipients (OR 1.92, 95% CI 1.41-2.62). In addition, there was a significant positive association between interactive technologies and upskilling of care workers (OR 2.44, 95% CI 1.58-3.79). Despite the benefits of welfare technology, the effects can also be negative, as shown by the findings on the use of digital documentation (OR 0.69, 95% CI 0.49-0.96), digital locks or cameras or sensors (OR 0.62, 95% CI 0.46-0.84), and the lower likelihood of participation. Training in the use of welfare technology was significantly associated with outcomes for care recipients (continuity of care: OR 2.02, 95% CI 1.53-2.66; participation: OR 1.91, 95% CI 1.45-2.51; reduction in loneliness: OR 1.74, 95% CI 1.31-2.30), as well as upskilling of care workers (OR 4.59, 95% CI 3.28-6.42). The interaction analyses showed that participants who had not received any training but used digital documentation reported favorable views on continuity of care and upskilling, whereas those who had received training expressed concerns about participation and addressing loneliness. Conclusions: The potential outcomes of welfare technology use in social care for older adults can vary with the types of technologies used. Care workers hold positive perceptions toward interactive technologies to improve care delivery outcomes and skill development. The findings on training and improved outcomes suggest that investing resources to promote training can reinforce positive experiences. While training had a positive impact, the interaction effects of digital documentation and training were mixed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".