Understanding User Preferences of Voice Assistant Answer Structures for Personal Health Data Queries
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.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".