Format guidelines to make them vivid, intuitive, and visual
Bibliographic record
Abstract
AIM: We present simple formatting rules derived from an extensive literature review that can improve the format of clinical practice guidelines (CPGs), and potentially increase the likelihood of being used. METHODS: We recently conducted a review of the literature from medicine, psychology, design, and human factors engineering on characteristics of guidelines that are associated with their use in practice, covering both the creation and communication of content. The formatting rules described in this article are derived from that review. RESULTS: The formatting rules are grouped into three categories that can be easily applied to CPGs: first, Vivid: make it stand out; second, Intuitive: match it to the audience's expectations, and third, Visual: use alternatives to text. We highlight rules supported by our broad literature review and provide specific 'how to' recommendations for individuals and groups developing evidence-based materials for clinicians. CONCLUSION: The way text documents are formatted influences their accessibility and usability. Optimizing the formatting of CPGs is a relatively inexpensive intervention and can be used to facilitate the dissemination of evidence in healthcare. Applying simple formatting principles to make documents more vivid, intuitive, and visual is a practical approach that has the potential to influence the usability of guidelines and to influence the extent to which guidelines are read, remembered, and used in practice.
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 | 0.065 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".