Community-based participatory research: a lifeline to achieve people-centered care
Bibliographic record
Abstract
People-centered care (PCC) represents a key paradigm shift in achieving universal health coverage and closing global public health divides. Amid growing global health disparities, shifts in epidemiological disease burden, and evolving sociopolitical contexts that affect healthcare delivery and research initiatives, there is an urgent need for public health scientists to develop community-rooted research strategies that uphold health promotion principles and sustain PCC. Community-based participatory research (CBPR) is a social justice approach that offers a distinct, equity-driven perspective on operationalizing PCC. CBPR fosters long-term, trust-based partnerships; centers the lived experiences and leadership of underserved populations; and co-develops sustainable health interventions that are culturally attuned to communities. Among participatory and community-engaged approaches, CBPR most closely aligns with—and can directly strengthen—the implementation of PCC principles. This paper presents an interprofessional and internationally relevant analysis of how CBPR can support PCC across clinical, public health, and policy domains. We begin by outlining foundational processes for establishing equitable academic–community partnerships. We then detail exemplar CBPR initiatives with racially and ethnically minoritized populations, as well as rural border and migrant communities, highlighting how these collaborations have advanced PCC goals. These exemplars, structured around key CBPR processes and mapped to PCC principles, form the basis of a conceptual blueprint for action. Next, we present a framework for applying CBPR to promote uptake of the World Health Organization’s integrated model for PCC, emphasizing its relevance with consideration to shifting policy and funding landscapes. Finally, we offer actionable recommendations for clinicians, researchers, community partners, health systems, and policy actors to integrate CBPR across the research continuum. To fully realize PCC in a rapidly changing world, researchers must shift from producing knowledge about communities to co-producing knowledge with them, ensuring that science is conducted in equal partnership with those most affected by structural health inequities.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".