A Health Equity–Oriented Research Agenda Requires Comprehensive Community Engagement
Bibliographic record
Abstract
Health policy and research communities have taken new approaches to addressing health equity, going beyond traditional methods that often excluded the contributions of health care consumers and persons with lived experience. This reevaluation has the potential to drive critical improvements in how we conduct research and innovate policy toward reducing health and health care disparities in the United States. Such considerations have led Fountain House, the founder of the Clubhouse model for peer-based psychosocial rehabilitation for persons with histories of serious mental illness, to incorporate community-based participatory action research (CBPAR) protocols within their research and service programs. The combination of CBPAR research methods within novel participatory care settings like Clubhouse programs presents unique and informative opportunities for the advancement of innovative health equity approaches to consumer empowerment in health care. In this piece, the authors (two staff researchers and one member researcher) propose how CBPAR research methods conducted in Clubhouses can uniquely advance equity-focused research methods, and how the benefit and enhancements from equity-focused research are continuously applied, practiced, and accountable to the communities within which the research is conducted. Embedding CBPAR practices within participatory care settings like Clubhouses, creates novel opportunities for research work to not only become more equitable but also become a part of the rehabilitative process, empowering the main beneficiaries of the research with the means to sustain and achieve further improvements for themselves. Such experiences are particularly important within rehabilitation settings, where there is a process of reclaiming empowerment and self-efficacy over a disability or illness and the social circumstances surrounding those conditions. Different stakeholders can all play important roles in advancing health equity-oriented research agendas by leveraging CBPAR principles. Academics and others in the research community can more comprehensively embed CBPAR methods into the design of their research studies. A critical link exists among how researchers conduct their studies, how providers organize care delivery and support, and how health plans pay for and evaluate care. CBPAR-generated research needs to fully engage clinical teams to ensure that ongoing community-involved care settings have direct applications to real-world care delivery. It is equally important that providers fully engage with their communities as they adjust their approaches to supporting the populations they serve.
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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 | 0.040 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".