Exploring usability characteristics in computer-based digital health technologies for family caregivers of people with chronic progressive conditions: A scoping review
Bibliographic record
Abstract
Objective To examine usability characteristics/attributes and evaluation methods incorporated in the design and/or evaluation of computer-based digital technologies for caregivers of people with chronic progressive conditions. Methods We searched Medline (OVID), PsycINFO (Ovid), CINAHL (EBSCO), and Web of Science Core Collection to identify relevant studies published from 2012 to May 2024. Two reviewers screened studies for eligibility, and extracted and synthesized data. Results Across 71 included studies, sample sizes ranged between 5 and 127. Most participants were caregivers of people with dementia ( n = 52, 73.2%). There was a mix of caregiving relationships across the 55 studies (77.5%) reporting this variable, with spouses most frequently included ( n = 51, 92.7%). Samples were predominantly female (72.1%), with mean ages between 43.0 and 76.7 years old. Most technologies were Website/Internet-based ( n = 43, 60.6%). Across the studies, we identified 31 distinct usability characteristics/attributes, with more than half of the studies ( n = 45, 63.4%) including at least three characteristics/attributes. However, nearly half of the studies ( n = 32, 45.1%) used a single usability evaluation method, predominantly inquiry-based interviews ( n = 15, 21.1%). Conclusion Findings reflect a narrow focus on middle-aged, female, and spousal caregivers, limiting the utility of digital health technologies for more diverse groups of caregivers. In addition to commonly used inquiry-based usability evaluation methods, user-testing, heuristic evaluation, and analytic modelling may offer more adaptive and holistic approaches to address a wider range of digital technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".