Mediating the Cognitive Walkthrough with Patient Groups to achieve Personalized Health in Chronic Disease Self-Management System Evaluation
Bibliographic record
Abstract
The cognitive walkthrough (CW) is a task-based, expert inspection usability evaluation method involving benefits such as cost effectiveness and efficiency. A drawback of the method is that it doesn't involve the user perspective from real users but instead is based on experts' predictions about the usability of the system and how users interact. In this paper, we propose a way of involving the user in an expert evaluation method by modifying the CW with patient groups as mediators. This along with other modifications include a dual domain session facilitator, specific patient groups and three different phases: 1) a preparation phase where suitable tasks are developed by a panel of experts and patients, validated through the content validity index 2) a patient user evaluation phase including an individual and collaborative process part 3) an analysis and coding phase where all data is digitalized and synthesized making use of Qualitative Data Analysis Software (QDAS) to determine usability deficiencies. We predict that this way of evaluating will utilize the benefits of the expert methods, also providing a way of including the patient user of these self-management systems. Results from this prospective study should provide evidence of the usefulness of this method modification.
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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 | 0.057 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".