Mobilizing the Power of Lived/Living Experiences to Improve Health Outcomes for all
Bibliographic record
Abstract
INTRODUCTION: Health Equity Assessments (HEAs) are decision-support frameworks or tools used to evaluate the equity impacts of policies, programmes and initiatives. However, HEAs are often conducted without meaningful engagement from the individuals and communities most affected by health inequities. This lack of social participation limits the relevance and effectiveness of HEAs, leaving systemic inequities unaddressed and opportunities for impactful change unrealized. An alternative is to involve people with diverse lived/living experiences in conducting and offering HEAs-so that people most impacted, and most excluded by decision-making can offer recommendations to improve the way they access and utilise care. METHODS: Equity Mobilizing Partnerships in Community (EMPaCT) is a scalable, participatory citizen engagement model that integrates lived/living experiences into the HEA process. EMPaCT's Five Steps to a Community-Engaged Health Equity Assessment (CEn-HEA) was co-designed with community members typically excluded from decision-making. This process fosters psychological safety, trust-building, and power-sharing between underserved communities and decision-makers. The CEn-HEA systematically analyzes inequities across downstream (individual), midstream (community), and upstream (structural) levels to generate actionable, equity-focused recommendations. RESULTS: The EMPaCT CEn-HEA framework produces context-specific recommendations that address immediate project needs while advancing long-term, systemic change. The framework is a participatory process that centres community voices, builds trust, amplifies lived/living expertise, and fosters equity-driven decision-making that can lead to measurable improvements in healthcare policies, programmes, and practices. CONCLUSION: In this paper, we examine the challenges and opportunities associated HEAs; introduce EMPaCT's CEn-HEA framework as a co-designed, innovative, and community-engaged approach to health equity analysis; and discuss methods for measuring and evaluating the health equity impacts of these efforts. PATIENT OR PUBLIC CONTRIBUTION: Patient and community involvement were central to the design, development and implementation of this project and resulting manuscript. Equity Mobilizing Partnerships in Community (EMPaCT), including its Community-Engaged Health Equity Assessment (CEn-HEA) framework, was co-created with diverse patient partners who have lived/living experiences of health inequities. In the preparation of this manuscript, patient partners were involved in codesign sessions to define the focus, structure and language of the manuscript. They collaborated in discussions to refine key concepts, articulate challenges and highlight solutions that are grounded in their lived realities. In the preparation of this manuscript, patient partners reviewed early drafts, contributed feedback to ensure accessibility and relevance of the content and shaped the actionable recommendations. This manuscript reflects EMPaCT's commitment to justice, inclusion and meaningful change.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".