Leadership in Community-Based Participatory Research: Individual to Collective
Bibliographic record
Abstract
Multi-sector collaborative partnerships hold much promise in tackling seemingly intractable and complex social issues. However, they often encounter many challenges in achieving their goals. Leadership can play an important role in reducing the impact of factors that threaten a multi-sector partnership’s success. Community-based participatory research (CBPR) partnerships are collaborative and, in many cases, multi-sectored. While there is a developing literature and practice on multi-sector, collaborative partnerships, leadership in CBPR is relatively unexplored, especially at various partnership stages (i.e., formation, implementation, maintenance, and accomplishment of goal). Through the method of focused ethnography, we explored the research question “How is leadership exercised during the formation stage of a CBPR partnership?” Eighteen partners (government, community, and university sectors) were interviewed about the leadership during the formation stage of their partnership, and data were qualitatively content-analyzed. Partners explained that leadership was exercised during the formation stage through (1) individual characteristics, (2) actions, and (3) as a collective. Our findings illustrate that CBPR leadership shares many of the characteristics of traditional leadership and adapts them to support the collaborative process of CBPR, leading to a collective form of leadership. These findings have implications for the study and practice of CBPR leadership.
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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 | 0.255 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".