Community-based knowledge transfer and exchange: Helping community-based organizations link research to action
Bibliographic record
Abstract
BACKGROUND: Community-based organizations (CBOs) are important stakeholders in health systems and are increasingly called upon to use research evidence to inform their advocacy, program planning, and service delivery efforts. CBOs increasingly turn to community-based research (CBR) given its participatory focus and emphasis on linking research to action. In order to further facilitate the use of research evidence by CBOs, we have developed a strategy for community-based knowledge transfer and exchange (KTE) that helps CBOs more effectively link research evidence to action. We developed the strategy by: outlining the primary characteristics of CBOs and why they are important stakeholders in health systems; describing the concepts and methods for CBR and for KTE; comparing the efforts of CBR to link research evidence to action to those discussed in the KTE literature; and using the comparison to develop a framework for community-based KTE that builds on both the strengths of CBR and existing KTE frameworks. DISCUSSION: We find that CBR is particularly effective at fostering a climate for using research evidence and producing research evidence relevant to CBOs through community participation. However, CBOs are not always as engaged in activities to link research evidence to action on a larger scale or to evaluate these efforts. Therefore, our strategy for community-based KTE focuses on: an expanded model of 'linkage and exchange' (i.e., producers and users of researchers engaging in a process of asking and answering questions together); a greater emphasis on both producing and disseminating systematic reviews that address topics of interest to CBOs; developing a large-scale evidence service consisting of both 'push' efforts and efforts to facilitate 'pull' that highlight actionable messages from community relevant systematic reviews in a user-friendly way; and rigorous evaluations of efforts for linking research evidence to action. SUMMARY: Through this type of strategy, use of research evidence for CBO advocacy, program planning, and service delivery efforts can be better facilitated and continually refined through ongoing evaluations of its impact.
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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 | 0.268 | 0.339 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.018 | 0.029 |
| Scholarly communication | 0.026 | 0.043 |
| Open science | 0.010 | 0.068 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".