Designing bowel preparation patient instructions to improve colon cancer detection
Bibliographic record
Abstract
Abstract Medical personnel usually write and design documents that inform physicians or patients about procedures or therapies. Document design, however, requires skills that are not normally applied, resulting in information that is often not used properly. This article describes a project developed by the Alberta Colorectal Cancer Screening Program. The goal was to help patients better prepare for their colonoscopies. The process started with an analysis of the existing documents, and the development of performance specifications based on the literature on legibility, reading comprehension, memorization and use of information, plain language, visual perception, page layout, and image use. The project included an iterative process of prototyping and testing that resulted in 23 design criteria. Each iteration was tested with users to ensure ease of use, completeness of information, and accuracy and clarity to facilitate adoption. The project helped reduce practice variation regarding bowel preparation in the province of Alberta, Canada. This project illustrates how information design can help healthcare organizations provide patient-centred care. Information design helps patients engage in their own caring process, by providing information that people can use, understand and apply. After 15 months of use, the document has been downloaded more than 48,000 times, suggesting a good physician reception.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".