Understanding User Preferences of Voice Assistant Answer Structures for Personal Health Data Queries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Voice assistants (VAs) are becoming ubiquitous within daily life, residing in homes, personal smart-devices, vehicles, and many other technologies. Designed for seamless natural language interaction, VAs empower users to ask questions and execute tasks without relying on graphical or tactile interfaces. A promising avenue for VAs is to allow people to ask personal health data questions. However, this functionality is currently not widely available and answer preferences to such questions have not been studied. We implemented a pseudo-VA that handles personal health data questions, answering in three unique styles: minimal, keyword, and full sentence. In two online user studies, 82 unique participants interacted with our VA, asking varying personal health data questions and ranking answer structures given. Our results show a strong preference for full sentence responses throughout. We find that even though full sentence answers have the longest mean response time, they are still found to provide high quality and optimal behaviour, while also being comprehensible and efficient. Furthermore, participants reported that for personal health question and answering, VAs should provide technical and efficient interactions rather than being social.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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