Understanding Current Needs and Future Expectations of Informal Caregivers for Technology to Support Health and Well-being: National Survey Study
Bibliographic record
Abstract
BACKGROUND: There are approximately 6.5 million informal (unpaid) caregivers in the United Kingdom. Each caregiver plays a critical role in the society, supporting the health and well-being of those who are ill, disabled, or older and who need frequent support. Digital technologies are becoming a ubiquitous part of everyday life for many, but little is known about the real-world impact of technology for those in a caring role, including the abilities of technologies to address the mental and physical impacts of caregiving. OBJECTIVE: This study aims to understand the current and future technology use of caregivers, including digital technologies used to care for themselves and the person they look after. METHODS: We codeveloped a wide range of questions with caregivers and care professionals and delivered this survey both on the web and in paper format (eg, using social networks such as Twitter alongside in-person events). Questions were focused on providing care and looking after caregiver health and well-being. Analyses focused on both quantitative outcomes (frequency counts and Likert questions) and explored free text entries (thematic analysis). RESULTS: From 356 respondents, we identified that caregivers were receptive to, and largely positive about current and future use of technology both for their own care and their caring role (eg, checking in from distance). There were notable concerns, including the risk that technology could replace human contact. We identified several key areas for future work, including communication with health and social care professionals, and the potential for technology to help caregivers with their own health. We also identified several stakeholders (eg, care workers, pharmacy staff, and general practitioners) who could act as suitable points for technology signposting and support. CONCLUSIONS: Caregivers are a transient, often difficult to reach population, and this work has collated a large body of knowledge across a diverse group of individuals. Many caregivers, like the rest of society, are realizing the benefits of using everyday technology to help deliver care. It is clear that there is already a high level of dependency on technologies, where future expectations will grow. However, many barriers to digital technology use remain, including a lack of ongoing technology support. Preventive measures linked to technology that can help look after a caregiver's own health appear acceptable, particularly for communicative tools. This collated caregiver knowledge is a call for all stakeholders-academics, policy makers, and practitioners-to take note of these specific challenges, and to ensure that caregiver voices are both heard and fully integrated within the emerging digital health agenda.
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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 | 0.000 | 0.000 |
| 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".