Understanding the Acceptance of an eHealth Technology in the Early Stages of Development: An End-User Walkthrough Approach and Two Case Studies
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Studies that focus on the acceptance of an electronic health (eHealth) technology generally make use of surveys. However, results of such studies hold little value for a redesign, as they focus only on quantifying end-user appreciation of general factors (eg, perceived usefulness). OBJECTIVE: We present a method for understanding end-user acceptance of an eHealth technology, early in the development process: The eHealth End-User Walkthrough. METHODS: During a walkthrough, a participant is guided by using the technology via a scenario, a persona, and a low-fidelity protoype. A participant is questioned about factors that may affect acceptance during and after the demonstration. We show the value of the method via two case studies. RESULTS: During the case studies, participants commented on whether they intend to use a technology and why they would (not) use its main features. They also provided redesign advice or input for additional functions. Finally, the sessions provide guidance for the generation of business models and implementation plans. CONCLUSIONS: The eHealth End-User Walkthrough can aid design teams in understanding the acceptance of their eHealth application in a very early stage of the design process. Consequently, it can prevent a mismatch between technology and end-users' needs, wishes and context.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it