Older Adults' Acceptance of Virtual Doctors: A Preliminary Investigation
Bibliographic record
Abstract
The use of virtual doctors is increasing; yet, we have not fully studied their impact and how they are perceived by the public, especially by digitally-marginalized users such as older adults. We also do not know how virtual doctors compare to other technology-mediated alternatives, like traditional telemedicine. More broadly, the factors leading to older users’ adoption of virtual doctors are not well understood. In other similar fields, users’ perceptions of early generations of conversational interfaces have been extensively studied. This raises the question of how virtual agents’ fidelity (e.g. video vs. speech-only) and agency (e.g. human vs. machine) influence confidence, comfort, and ease of use in target groups like older adults. To fill these knowledge gaps, I have conducted a mixed-methods study with older adults in which they engaged with different versions of telemedicine setups. The versions varied along the dimensions of fidelity (e.g. video vs. speech-only) and agency (e.g. human vs. machine). Analysis of interview and survey data shows that older adults were most confident and comfortable with speech-only machine-powered interfaces for general healthcare information, and with video-based link with human doctors for specific healthcare information. The findings also show that in order for virtual doctor systems to be accepted by older adults, they need to do the following: complement older adults’ visits to their existing doctors, fit their existing information practices for gathering healthcare information, and have perceived value compared to their currently available alternatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".