The Use of Technology to Deliver In-Home Aged Care Services: Mixed Methods Study of Australian Staff Perspectives
Bibliographic record
Abstract
Background: With a global aging population, technology has been proposed as a solution to address the growing demand for services in the in-home aged care sector. Despite the potential of technology, there are difficulties when implementing technology into routine care delivery. There is a lack of evidence regarding the specific factors affecting technology use in the in-home aged care setting from the perspective of the direct care workforce. Objective: This study aimed to understand in-home aged care staff members' views of (1) the digital enablement potential of direct in-home care tasks, (2) benefits and drawbacks of technology use, and (3) enablers and barriers for technology use in Australian in-home aged care. Methods: An explanatory sequential mixed methods research design was used, with a cross-sectional survey and semistructured staff interviews. Participants were recruited from in-home aged care staff members working at a national Australian in-home health and aged care organization. Results: In total, 226 participants completed the survey, and 18 participants completed the interviews. Overall, participants felt that many care tasks within in-home aged care could be digitally enabled, with more than half (56%) of the common direct care tasks identified as being likely to be digitally enabled. Participants also discussed a range of quality of care-, staff-, and organization-related benefits and drawbacks in the use of technology. Finally, participants agreed that most of the researcher-proposed enablers and barriers were important, while suggesting additional enablers and barriers such as client preferences regarding technology use and poor data connectivity. Conclusions: This study provides insight into staff members' views regarding the use of technology to deliver in-home aged care services. The results could help inform technology developers and in-home aged care providers, providing key information to guide technology implementation into care delivery. Further research is required to ensure that appropriate strategies are available to ensure successful implementation of technology into in-home aged care.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".