eHealth Technology Competencies for Health Professionals Working in Home Care to Support Older Adults to Age in Place: Outcomes of a Two-Day Collaborative Workshop
Bibliographic record
Abstract
BACKGROUND: The demand for care is increasing, whereas in the near future the number of people working in professional care will not match with the demand for care. eHealth technology can help to meet the growing demand for care. Despite the apparent positive effects of eHealth technology, there are still barriers to technology adoption related to the absence of a composite set of knowledge and skills among health care professionals regarding the use of eHealth technology. OBJECTIVE: The objective of this paper is to discuss the competencies required by health care professionals working in home care, with eHealth technologies such as remote telecare and ambient assisted living (AAL), mobile health, and fall detection systems. METHODS: A two-day collaborative workshop was undertaken with academics across multiple disciplines with experience in working on funded research regarding the application and development of technologies to support older people. RESULTS: The findings revealed that health care professionals working in home care require a subset of composite skills as well as technology-specific competencies to develop the necessary aptitude in eHealth care. This paper argues that eHealth care technology skills must be instilled in health care professionals to ensure that technologies become integral components of future care delivery, especially to support older adults to age in place. Educating health care professionals with the necessary skill training in eHealth care will improve service delivery and optimise the eHealth care potential to reduce costs by improving efficiency. Moreover, embedding eHealth care competencies within training and education for health care professionals ensures that the benefits of new technologies are realized by casting them in the context of the larger system of care. These care improvements will potentially support the independent living of older persons at home. CONCLUSIONS: This paper describes the health care professionals' competencies and requirements needed for the use of eHealth technologies to support elderly adults to age in place. In addition, this paper underscores the need for further discussion of the changing role of health care professionals working in home care within the context of emerging eHealth care technologies. The findings are of value to local and central government, health care professionals, service delivery organizations, and commissioners of care to use this paper as a framework to conduct and develop competencies for health care professionals working with eHealth technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".