Equity-Mobilizing Partnerships in Community (EMPaCT): Co-Designing Patient Engagement to Promote Health Equity
Bibliographic record
Abstract
Equity-Mobilizing Partnerships in Community (EMPaCT) is a novel approach to patient engagement that centres diverse lived experiences and promotes equity-oriented and inclusive partnerships.As an independent community table, EMPaCT is made up primarily of patients/diverse members of community.Researchers and other decision makers come to this table with their projects to learn how to make their project more inclusive and equitable.In this paper, we detail how we used participatory co-design to define, build and grow EMPaCT as an innovative and scalable patient partnership model that promotes bottom-up action for health equity. Key Points• Equity-oriented patient partnerships can be co-designed together with members of community so that the needs and priorities of community drive the process and outcomes of engagement.• Community-led and community-driven patient engagement tables, such as Equity-Mobilizing Partnerships in Community (EMPaCT), can be a useful resource in a learning health system for decision makers who currently have few ways to engage with diverse patients.• EMPaCT uses tools such as Health Equity Assessments and consultations to help decision makers make their projects more inclusive and equitable.
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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 | 0.055 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".