Engaging Everyone: Co-building diverse and inclusive patient partnerships to promote health equity.
Bibliographic record
Abstract
Background: To address the challenge of engaging diverse people, discover a novel model of engagement where diverse members of community bring a health equity lens to healthcare projects, policies and research. Workshop description: This workshop is designed to introduce participants to challenges and opportunities in diverse patient engagement. Participants will learn about the Equity-Mobilizing Partnerships in Community (EMPaCT) model. Through group discussions, participants will explore how they can adapt the EMPaCT approach to their own settings. Through tools and resources developed by EMPaCT, participants will learn how to co-design sustainable and scalable models of patient engagement to promote health equity. In particular, participants will learn about: 1.Conceptual frameworks that make explicit the relationships between social, political and economic inequities and health outcomes; 2.Strategies to involve communities experiencing the most inequities in policy and decision-making; and 3.Methods to design programs that are scalable. Intended audience: Anyone who is: 1.interested in learning how to engage in patient partnerships that promote health equity; 2.interested in engaging with diverse and seldom-heard communities for practice, research or policy development; 3.involved in system-level healthcare redesign and resourcing. Workshop overview & agenda (90 mins workshop) 1.Introductions (10 mins) – Alies, Ambreen, Emily 2.Section: Getting to know the audience (10 mins ) – Emily Approach: Interactive polls. Responses reviewed in real-time. Outcome: Understand which stakeholder category participants identify with and their experiences in patient engagement including engagement with diverse voices as well as level of decision-making authority. 3.Section: Introduction to the challenges and opportunities in diverse patient engagement and the need for scalable equity-oriented patient partnerships (10 mins) – Ambreen Approach: Presentation by workshop facilitators. Outcome: Participants will have foundational knowledge needed to engage in interactive workshop. 4.Section: Identifying and using conceptual frameworks that recognize how health inequities drive health outcomes (15 mins) – Emily Approach: Overview of frameworks and interactive discussion using Padlet-based questions. Outcome: Participants will engage in learning and discussion about health and social inequities. 5.Section: Involving communities experiencing the most inequities in policy and decision-making (15 mins) – Alies Approach: Facilitators will share how EMPaCT was co-designed using an equity-oriented lens to patient engagement. Participants will learn how to apply the same lens to their setting using interactive group discussion through Padlet. Outcome: Participants will learn about different ways to involve communities experiencing the most inequities in policy and decision-making. 6.Section: Designing programs that are scalable and sustainable (15 mins) – Ambreen Approach: Facilitators will share how EMPaCT was co-developed as a scalable unit. Participants will engage in interactive discussions through Padlet to identify the resources available to them to co-create a scalable unit in their setting. Outcome: Participants will learn about different ways to scale and sustain patient engagement activities that promote health equity. 7.Closing summary (15 mins) – Alies
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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 | 0.025 | 0.032 |
| 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 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".