User Experience of a Chatbot Questionnaire Versus a Regular Computer Questionnaire: Prospective Comparative Study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Respondent engagement of questionnaires in health care is fundamental to ensure adequate response rates for the evaluation of services and quality of care. Conventional survey designs are often perceived as dull and unengaging, resulting in negative respondent behavior. It is necessary to make completing a questionnaire attractive and motivating. OBJECTIVE: The aim of this study is to compare the user experience of a chatbot questionnaire, which mimics intelligent conversation, with a regular computer questionnaire. METHODS: The research took place at the preoperative outpatient clinic. Patients completed both the standard computer questionnaire and the new chatbot questionnaire. Afterward, patients gave their feedback on both questionnaires by the User Experience Questionnaire, which consists of 26 terms to score. RESULTS: The mean age of the 40 included patients (25 [63%] women) was 49 (SD 18-79) years; 46.73% (486/1040) of all terms were scored positive for the chatbot. Patients preferred the computer for 7.98% (83/1040) of the terms and for 47.88% (498/1040) of the terms there were no differences. Completion (mean time) of the computer questionnaire took 9.00 minutes by men (SD 2.72) and 7.72 minutes by women (SD 2.60; P=.148). For the chatbot, completion by men took 8.33 minutes (SD 2.99) and by women 7.36 minutes (SD 2.61; P=.287). CONCLUSIONS: Patients preferred the chatbot questionnaire over the computer questionnaire. Time to completion of both questionnaires did not differ, though the chatbot questionnaire on a tablet felt more rapid compared to the computer questionnaire. This is an important finding because it could lead to higher response rates and to qualitatively better responses in future questionnaires.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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