Community-based participatory research (CBPR): Towards equitable involvement of community in psychology research.
Bibliographic record
Abstract
Community-based participatory research (CBPR) answers the call for more patient-centered, community-driven research approaches to address growing health disparities. CBPR is a collaborative research approach that equitably involves community members, researchers, and other stakeholders in the research process and recognizes the unique strengths that each bring. The aim of CBPR is to combine knowledge and action to create positive and lasting social change. With its origins in psychology, sociology, and critical pedagogy, CBPR has become a common research approach in the fields of public health, medicine, and nursing. Although it is well aligned with psychology's ethical principles and research aims, it has not been widely implemented in psychology research. The present article introduces CBPR to a general psychology audience while considering the unique aims of and challenges in conducting psychology research. In this article, we define CBPR principles, differentiate it from a more traditional psychology research approach, retrace its historical roots, provide concrete steps for its implementation, discuss its potential benefits, and explore practical and ethical challenges for its integration into psychology research. Finally, we provide a case study of CBPR in psychology to illustrate its key constructs and implementation. In sum, CBPR is a relevant, important, and promising research framework that may guide the implementation of more effective, culturally appropriate, socially just, and sustainable community-based psychology research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.384 | 0.379 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.016 | 0.043 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".