User-Centered Design and Printed Educational Materials: A Focus Group Study of Primary Care Physician Preferences
Bibliographic record
Abstract
INTRODUCTION: It is challenging for primary care physicians (PCPs) to review and apply the growing amount of clinical evidence available. Printed educational materials (PEMs), which synthesize evidence, are often ineffective at improving knowledge, possibly due to poor design and limited uptake. In this study, we collected PCP preferences for the design and content of physician-oriented PEMs and determined key attributes that may increase their usability and uptake. METHODS: We held 90-minute focus groups with PCPs in Toronto, ON, Canada. Focus groups included discussion about whether and how participants use PEMs, feedback on three examples of PEMs, and a discussion on general format and design preferences in PEMs. We analyzed focus group transcripts using a thematic analysis and summarized results in a list of user preferences. RESULTS: Four focus groups were held with 13 PCPs. We found that participants only read PEMs relevant to their patients and prefer short, concise documents, with links to sources that can provide more detailed information. Simplicity of materials was important, with many participants preferring PEMs without lengthy backgrounds or scientific explanations. Most participants wanted to see key messages highlighted to easily assess the relevance of the materials to their practice. Some participants shared physician-oriented PEMs with patients. DISCUSSION: This study shows that PCPs may prefer shorter, simpler, and more concise documents that have less scientific detail but provide references to further information sources. It is important to understand end user preferences for the design and content of these materials to enhance their uptake.
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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 | 0.040 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".