Clinicians’ experience with infographic summaries from the BMJ Rapid Recommendations: a qualitative user-testing study among residents and interns at a large teaching hospital in Switzerland
Bibliographic record
Abstract
OBJECTIVE: Clinicians need trustworthy clinical practice guidelines to succeed with evidence-based diagnosis and treatment at the bedside. The BMJ Rapid Recommendations explore innovative ways to enhance dissemination and uptake, including multilayered interactive infographics linked to a digitally structured authoring and publication platform (the MAGICapp). We aimed to assess user experiences of physicians in training in various specialties when they interact with these infographics. DESIGN: We conducted a qualitative user-testing study to assess user experience of a convenience sample of physicians in training. User testing was carried out through guided think-aloud sessions. We assessed six facets of user experience using a revised version of Morville's framework: usefulness, understandability, usability, credibility, desirability and identification. SETTING: Setting include Geneva's University Hospital, a large teaching hospital in Switzerland. PARTICIPANTS: Participants include a convenience sample of residents and interns without restriction regarding medical field or division of care. RESULTS: Most users reported a positive experience. The infographics were understandable and useful to rapidly grasp the key elements of the recommendation, its rationale and supporting evidence, in a credible way. Some users felt intimidated by numbers or the amount of information, although they perceived there could be a learning curve while using generic formats. Plain language summaries helped complement the visuals but could be further highlighted. Despite their generally positive experience, several users had limited understanding of key GRADE (Grading of Recommendations Assessment, Development and Evaluation) domains of the quality of evidence and remained uncertain by the implication of weak or conditional recommendations. CONCLUSION: Our study allowed to identify several aspects of guideline formats that improve their understandability and usefulness. Guideline organisations can use our findings to adapt their presentation format to enhance their dissemination and uptake in clinical practice. Avenues for research include the interplay between infographics and the digital authoring platform, multiple comparisons and living guidelines.
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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".