P081 Design Of Physician Printed Educational Materials: Making Good Ideas Stick
Bibliographic record
Abstract
Background It is difficult to communicate new and complex clinical evidence to physicians already experiencing information overload. Proper use of design principles may increase uptake of guidelines and other printed educational materials (PEM) and improve practice. Objectives We aimed to determine whether physician-oriented PEMs are created in accordance with design principles. Methods We analysed PEMs identified in a 2012 Cochrane review of their effect on professional and patient outcomes and developed a checklist of design principles based on a literature review of clinical guideline implementability. Two analysts independently evaluated each PEM to determine how design principles were applied. Results Though the sample consisted of PEMs designed and developed to influence care, no single PEM scored well across all categories. Some PEMs failed to differentiate major recommendations and did not present them in a stepwise fashion. Most used clear and easy to read text, but highlighting was often inappropriate. Some algorithms lacked logic and consistency. Images were poorly designed and used, which may distract and confuse the reader. Discussion Design principles are not consistently applied in the development of PEMs and improvements are needed to images, presentation of recommendations, and usability of algorithms. Improvements to the design of PEMs may influence their uptake by combating information overload and increasing their perceived ease of use and perceived usefulness. Implications for Guideline Developers/Users Those who create guidelines and other PEMs consider some design principles, but do not implement them consistently. Our checklist can assist guideline developers in employing a range of design principles.
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 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 | 0.000 |
| 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) | 0.001 | 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".