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Record W4408559460 · doi:10.1111/hex.70212

Mobilizing the Power of Lived/Living Experiences to Improve Health Outcomes for all

2025· article· en· W4408559460 on OpenAlexafffund
Ambreen Sayani, Linda Monteith, Anam Shahil‐Feroz, Diya Srinivasan, Isra Amsdr, Fatah Awil, Emily Cordeaux, Victoria Garcia, R. Hinds, Tara Jeji, Omar Khan, Bee Wah Lee, Mursal Musawi, Jill Robinson, Staceyan Sterling, Dean Wardak, Kelly Wu, Mohadessa Khawari, Meghan Gilfoyle, Alies Maybee

Bibliographic record

VenueHealth Expectations · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsOntario Stroke NetworkWestern UniversityPublic Health OntarioWomen's College HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsEquity (law)Health equityPublic relationsCommunity-based participatory researchCitizen journalismContext (archaeology)Health careBusinessParticipatory action researchPsychologySociologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.296
GPT teacher head0.496
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes2
Has abstractyes

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