Underestimated Factors Regarding the Use of Technology in Daily Practice of Long-Term Care: Qualitative Study Among Health Care Professionals
Bibliographic record
Abstract
BACKGROUND: Increasing life expectancy is resulting in a growing demand for long-term care; however, there is a shortage of qualified health care professionals (HCPs) to deliver it. If used optimally, technology can provide a solution to this challenge. HCPs play an important role in the use of technology in long-term care. However, technology influences several core aspects of the work that HCPs do, and it is therefore important to have a good understanding of their viewpoint regarding the use of technology in daily practice of long-term care. OBJECTIVE: The aim of this study was to identify the factors that HCPs consider as relevant for using technology in daily practice of long-term care. METHODS: In this qualitative study, 11 focus groups were organized with 73 HCPs. The focus group discussions were guided by an innovative game, which was specifically developed for this study. The content of the game was categorized into 4 categories: health care technology and me; health care technology, the patient, and me; health care technology, the organization, and me; and facilitating conditions. The perspectives of HCPs about working with technology were discussed based on this game. The focus groups were recorded and transcribed, followed by an inductive thematic analysis using ATLAS.ti 9x (ATLAS.ti Scientific Software Development GmbH). RESULTS: Overall, 2 main domain summaries were developed from the data: technology should improve the quality of care and acceptance and use of technology in care. The first factor indicates the need for tailored and personalized care and balance between human contact and technology. The second factor addresses several aspects regarding working with technology such as trusting technology, learning to work with technology, and collaboration with colleagues. CONCLUSIONS: HCPs are motivated to use technology in daily practice of long-term care when it adds value to the quality of care and there is sufficient trust, expertise, and collaboration with colleagues. Their perspectives need to be considered as they play a crucial part in the successful use of technology, transcending their role as an actor in implementation. On the basis of the findings from this study, we recommend focusing on developing technology for situations where both efficiency and quality of care can be improved; redefining the roles of HCPs and the impact of technology hereon; involving HCPs in the design process of technology to enable them to link it to their daily practice; and creating ambassadors in care teams who are enthusiastic about working with technology and can support and train their colleagues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".