1000 cups of coffee: a call for intentional relationship-building in behavioral science through community-based participatory research
Bibliographic record
Abstract
Abstract Behavioral scientists increasingly recognize the importance of community engagement in the process toward designing impactful, equitable, and sustainable interventions. Yet, the academic structures that govern research timelines and outputs often undervalue the slow, relational labor required to form meaningful Community-Academic Partnerships (CAPs). This commentary uses the metaphor of "1000 cups of coffee" to capture the time-intensive, trust-building processes foundational to Community-Based Participatory Research (CBPR). We argue that without deep-rooted relationships, the process of co-design and intervention development may become nominal, irrelevant, or ineffective. Drawing on our own examples of creating a pan-Canadian community of practice advancing newcomer sport and physical activity behaviors, we highlight how we have embedded CBPR into our own research practice. By committing to authentic partnerships, behavioral scientists can ensure that their work is contextually grounded, culturally relevant, and eventually more impactful.This is a pre-copyedited, author-produced PDF of an article accepted for publication in Translational Behavioral Medicine following peer review. The version of record [1000 cups of coffee: a call for intentional relationship-building in behavioral science through community-based participatory research. Translational Behavioral Medicine 15, 1 (2025)] is available online at: https://doi.org/10.1093/tbm/ibaf041. Deposited by shareyourpaper.org and openaccessbutton.org. We've taken reasonable steps to ensure this content doesn't violate copyright. However, if you think it does you can request a takedown by emailing help@openaccessbutton.org.
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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 | 0.326 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.088 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.010 | 0.027 |
| Research integrity | 0.032 | 0.056 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".