Successful Strategies to Engage Research Partners for Translating Evidence into Action in Community Health: A Critical Review
Bibliographic record
Abstract
OBJECTIVES: To undertake a critical review describing key strategies supporting development of participatory research (PR) teams to engage partners for creation and translation of action-oriented knowledge. METHODS: Sources are four leading PR practitioners identified via bibliometric analysis. Authors' publications were identified in January 1995-October 2009 in PubMed, Embase, ISI Web of Science and CAB databases, and books. Works were limited to those with a process description describing a research project and practitioners were first, second, third, or last author. RESULTS: Adapting and applying the "Reliability Tested Guidelines for Assessing Participatory Research Projects" to retained records identified five key strategies: developing advisory committees of researchers and intended research users; developing research agreements; using formal and informal group facilitation techniques; hiring co-researchers/partners from community; and ensuring frequent communication. Other less frequently mentioned strategies were also identified. CONCLUSION: This review is the first time these guidelines were used to identify key strategies supporting PR projects. They proved effective at identifying and evaluating engagement strategies as reported by completed research projects. Adapting these guidelines identified gaps where the tool was unable to assess fundamental PR elements of power dynamics, equity of resources, and member turnover. Our resulting template serves as a new tool to measure partnerships.
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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 | 0.194 | 0.331 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.033 | 0.023 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".