Usability testing of ANSWER: a web-based methotrexate decision aid for patients with rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Decision aids are evidence-based tools designed to inform people of the potential benefit and harm of treatment options, clarify their preferences and provide a shared decision-making structure for discussion at a clinic visit. For patients with rheumatoid arthritis (RA) who are considering methotrexate, we have developed a web-based patient decision aid called the ANSWER (Animated, Self-serve, Web-based Research Tool). This study aimed to: 1) assess the usability of the ANSWER prototype; 2) identify strengths and limitations of the ANSWER from the patient's perspective. METHODS: The ANSWER prototype consisted of: 1) six animated patient stories and narrated information on the evidence of methotrexate for RA; 2) interactive questionnaires to clarify patients' treatment preferences. Eligible participants for the usability test were patients with RA who had been prescribed methotrexate. They were asked to verbalize their thoughts (i.e., think aloud) while using the ANSWER, and to complete the System Usability Scale (SUS) to assess overall usability (range = 0-100; higher = more user friendly). Participants were audiotaped and observed, and field notes were taken. The testing continued until no new modifiable issues were found. We used descriptive statistics to summarize participant characteristics and the SUS scores. Content analysis was used to identified usability issues and navigation problems. RESULTS: 15 patients participated in the usability testing. The majority were aged 50 or over and were university/college graduates (n = 8, 53.4%). On average they took 56 minutes (SD = 34.8) to complete the tool. The mean SUS score was 81.2 (SD = 13.5). Content analysis of audiotapes and field notes revealed four categories of modifiable usability issues: 1) information delivery (i.e., clarity of the information and presentation style); 2) navigation control (i.e., difficulties in recognizing and using the navigation control buttons); 3) layout (i.e., position of the videos, text, diagrams and navigation buttons); 4) aesthetic (i.e., the colour, look and feel of the online tool). CONCLUSIONS: Although the SUS score indicated high usability before and after major modification, findings from the think-aloud sessions illustrated areas that required further refinement. Our results highlight the importance of formative evaluation in usability testing.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".