Individual differences in views toward healthcare conversational agents: A cross-sectional survey study
Bibliographic record
Abstract
Background and Objective: To date, there has been limited research on people's attitudes and design preferences with respect to conversational agents (CAs) that are used for healthcare. Individual differences in attitudes and design preferences have received particularly little attention. The purpose of this study was to gain greater insight into this topic. Methods: We recruited American and Canadian residents through the online research platform Prolific. Participants completed a cross-sectional survey assessing demographic, personality, and health factors, as well as attitudes and design preferences with respect to healthcare CAs. Hierarchical regressions were used to determine demographic, personality, and health predictors of attitudes and design preferences. Results: = 12.94) were included in the analysis. Participants tended to report slightly positive attitudes toward healthcare CAs, with more positive attitudes among American residents and people with lower income, lower education levels, and higher levels of the personality factor conscientiousness. In general, participants preferred CAs that use text communication, have unrestricted language input, are disembodied, and simulate health professionals in their presentation. CAs that use text communication were preferred to a greater degree among people with higher levels of digital health literacy, and disembodied CAs were preferred to a greater degree among people with lower levels of conscientiousness. Conclusion: The results of this study provide insight into people's attitudes and design preferences with respect to healthcare CAs. This information will help guide developers on how to better design and market CAs for the health sector, which may increase people's adoption and use of these programs.
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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".