Application of Anti-Stigma Design Heuristics for Usability Inspection
Bibliographic record
Abstract
User interface evaluation has become important in developing usable health care technologies. Although usability engineering methods have been applied in the design and evaluation of health care software, available heuristics focus on task-work aspects and do not address stigma associated with many health conditions. We used a previous set of heuristics and propose a new set of anti-stigma heuristics to evaluate stigmatization in health care websites. The extended set of heuristics were concurrently applied in a heuristic evaluation and a cognitive walkthrough to evaluate an endometriosis and sexual pain website. The walkthrough involved 5 tasks that required 21 actions to execute. Twenty-six usability problems were identified and recommendations for re-design were made to the design team before end-user testing. The anti-stigma heuristics received worse ratings than the traditional heuristics, resulting in several design changes that might otherwise have been missed. Thus, the new anti-stigma heuristics were a valuable contribution.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".