People's Perceptions and Expectations of Assistive Health-Enabling Technologies: An Empirical Study in Germany
Bibliographic record
Abstract
Demographic shifts and their consequences will lead to changes in the way health care is provided. Although assistive health-enabling technologies are regarded as one means to support these changes, they are minimally used, despite the maturity of the underlying technologies. This may partly be attributable to a disregard of users' needs and preferences. The aim of this article is to assess acceptance of health-enabling technologies with regard to their perceived usefulness, risks, and people's readiness to actually use them. Furthermore, we attempted to find out to whom individuals would entrust their health information, and what their basic fears are. We used a questionnaire presenting four exemplary technologies: emergency call systems, videophones, activity and health status monitoring. We conducted 147 face-to-face interviews and analyzed the results using descriptive statistics. Emergency call systems, health status and activity monitoring were rated as useful or very useful, videophones as hardly useful. Intrusion into one's privacy was the most prominent concern. Regarding fears in old age, people were mostly afraid of diseases and loss of independence. They would entrust their medical data to their physicians rather than relatives or caregivers. This study may contribute to systematic analyses of users' perceptions and preferences concerning assistive health-enabling 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".