Engagement strategies that foster community self-determination in participatory research: Insider ownership through outsider championship
Bibliographic record
Abstract
Background: In order to maximize the benefits of community-based participatory research, effective ownership over the research process must be at least equally in the hands of the community. A previous social network analysis documented that the participatory research process shifted ownership from academic to community partners, but did not show what actions and strategies fostered this shift. Objectives: This study follows the trajectory of a community-academic partnership and asks, from the perspective of the project stakeholders, which actions and strategies over the lifespan of the research led to the observed shift in ownership and decision-making from the original external academics to the community stakeholders? Methods: Qualitative description using inductive thematic analysis. One academic and five community stakeholders identified as central in a previous social network analysis, participated in retrospective, semi-structured interviews. Results: Actions deemed to have fostered the observed shift in ownership included: existence of a strong champion; stimulating 'outside' ideas; emergence of core people; alignment of project goals with stakeholders' professional roles; involving the right people; personal qualities of the champion; trust-building; and active use of participatory engagement strategies. Conclusion: Although communities must take ownership over the research process to assure sustained action and change, a strong, trusted and accepted outside champion who actively enacts participatory engagement strategies can facilitate the participatory process and provide community stakeholders the time and support they need to achieve meaningful and sustained leadership roles. These findings have implications for how partnership research is designed and implemented, both in community and in clinical organisational settings.
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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 | 0.142 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".