Community-Based Participatory Research: Lessons and Challenges. Symposium Special Communication
Bibliographic record
Abstract
Community-based participatory research (CBPR) is grounded in the commitment of co-creation and co-development of research that is for, by, and with the population it is intended to impact. Translational oral health researchers can harness this research approach when conceptualizing innovations and interventions, especially in those contexts where populations have been made systemically and historically vulnerable. This commentary highlights lessons shared and challenges presented when implementing CBPR, derived from a 2024 IADR/AADOCR/CADR General Session & Exhibition symposium. The presenters shared numerous considerations when planning CBPR, such as integrating an equity lens in research, the necessity of community partnerships and trust-building, and the significance of adopting principles and criteria that are developed by the communities one works with and are therefore relevant and applicable to their particular oral health needs. Additionally, the panel of speakers and symposium attendants discussed ways of ensuring the sustainability of interventions and the integration of worldviews other than that of the researchers into CBPR. Oral health scientists and program implementers working with communities' interests in mind must be alert of how best to harness CBPR to adequately respect self-determination and governance of all peoples and, in this manner, develop strategies that are adopted and valued by the communities they intend to serve.Knowledge Transfer Statement:Community-based participatory research is an equitable and wholesome approach that aims to respectfully collaborate with the communities that it seeks to impact. It offers everyone a seat at the table when trying to create transformative clinical, behavioral, and health services change. Oral health scientists and program implementers can apply this framework for research and programming in communities where past approaches have not necessarily benefited the peoples or their communities in an equitable manner.
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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 | 0.269 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.026 | 0.025 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".