Using Cognitive Work Analysis and a Persuasive Design Approach to Create Effective Blood Pressure Management Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When developing patient self-management applications, designs must motivate patients to engage with the intervention so as to increase their medical adherence. This paper hypothesizes that for the analysis and design of these technologies, a Cognitive Work Analysis (CWA) approach would benefit from the additional insight provided by a Persuasive Design (PD) framework. The results of this combined approach are shown to be valuable in informing an Ecological Interface Design (EID) of self-management systems. Benefits come in two ways: First, by improving patients’ understanding of their disease and how to manage it, and second by increasing their motivation to self-monitor. The next step of this research will be to devise a set of design guidelines that can be used to create effective patient self-management systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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