Engaging diverse patients in a diverse world: the development and preliminary evaluation of educational modules to support diversity in patient engagement research
Bibliographic record
Abstract
BACKGROUND: Current practices for engaging patients in patient-oriented research (POR) result in a narrow pool of patient perspectives being reflected in POR. This project aims to address gaps in methodological knowledge to foster diversity in POR, through the co-design and evaluation of a series of educational modules for health researchers in British Columbia, Canada. METHODS: Modules were co-created by a team of academic researchers and patient partners from hardly-reached communities. The modules are presented using the Tapestry Tool, an interactive, online educational platform. Our evaluation framework focused on engagement, content quality, and predicted behavior change. The User Engagement Scale short form (UES-SF) measured participants' level of engagement with the modules. Survey evaluation items assessed the content within the modules and participants' perceptions of how the modules will impact their behavior. Evaluation items modeled on the theory of planned behavior, administered before and after viewing the modules, assessed the impact of the modules on participants' perceptions of diversity in POR. RESULTS: Seventy-four health researchers evaluated the modules. Researchers' engagement and ratings of module content were high. Subjective behavioral control over fostering diversity in POR increased significantly after viewing the modules. CONCLUSIONS: Our results suggest the modules may be an engaging way to provide health researchers with tools and knowledge to increase diversity in health research. Future studies are needed to investigate best practices for engaging with communities not represented in this pilot project, such as children and youth, Indigenous Peoples, and Black communities. While educational interventions represent one route to increasing diversity in POR, individual efforts must occur in tandem with high-level changes that address systemic barriers to engagement.
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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 | 0.052 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".