Redesigning printed educational materials for primary care physicians: design improvements increase usability
Bibliographic record
Abstract
BACKGROUND: Printed educational materials (PEMs) are a frequently used tool to disseminate clinical information and attempt to change behavior within primary care. However, their effect on clinician behavior is limited. In this study, we explored how PEMs can be redesigned to better meet the needs of primary care physicians (PCPs) and whether usability and selection can be increased when design principles and user preferences are used. METHODS: We redesigned a publicly available PEM using physician preferences, design principles, and graphic designer support. We invited PCPs to select their preferred document between the redesigned and original versions in a discrete choice experiment, followed by an assessment of usability with the System Usability Scale and a think aloud process. We conducted this study in both a controlled and opportunistic setting to determine whether usability testing results vary by study location. Think aloud data was thematically analyzed, and results were interpreted using the Technology Acceptance Model. RESULTS: One hundred and eighty four PCPs participated in the discrete choice experiment at the 2014 Family Medicine Forum, a large Canadian conference for family physicians. Of these, 87.7 % preferred the redesigned version. Follow-up interviews were held with a randomly selected group of seven participants. We repeated this in a controlled setting in Toronto, Canada, with a set of 14 participants. Using the System Usability Scale, we found that usability scores were significantly increased with the redesign (p < 0.001). We also found that when PCPs were given the choice between the two versions, they selected the redesigned version as their preferred PEM more often than the original (p < 0.001). Results did not appear to differ between the opportunistic and controlled setting. We used the results of the think aloud process to add to a list of end user preferences developed in a previous study. CONCLUSIONS: We found that redesigning a PEM with user preferences and design principles can improve its usability and result in the PEM being selected more often than the original. We feel this finding supports the involvement of the user, application of design principles, and the assistance of a graphic designer in the development of PEMs.
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".