Involving Vulnerable User Groups for Designing eHealth: A Designers’ Perspective
Bibliographic record
Abstract
Involving users in the design of eHealth systems for the whole population can be difficult due to the wide range of potential users. This is particularly true in designing applications that will be used by vulnerable user groups, such as those of higher age, those with lower income or education, and those living alone or in rural areas. In this paper, we describe a qualitative interview study of designers involved in designing eHealth applications. The focus of the study was on how user needs and requirements are being incorporated into the design of such systems. The findings indicate the importance of understanding the accessibility needs of vulnerable populations. The paper describes a range of challenges reported in making eHealth more accessible to a wider range of users, including vulnerable groups.
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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 | 0.048 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".