Networking and Knowledge Exchange to Promote the Formation of Transdisciplinary Coalitions and Levels of Agreement Among Transdisciplinary Peer Reviewers
Bibliographic record
Abstract
CONTEXT: Funding for transdisciplinary chronic disease prevention research has increased over the past decade. However, few studies have evaluated whether networking and knowledge exchange activities promote the creation of transdisciplinary teams to successfully respond to requests for proposals (RFPs). Such evaluations are critical to understanding how to accelerate the integration of research with practice and policy to improve population health. OBJECTIVE: To examine (1) the extent of participation in pre-RFP activities among funded and nonfunded transdisciplinary coalitions that responded to a RFP for cancer and chronic disease prevention initiatives and (2) levels of agreement in proposal ratings among research, practice, and policy peer reviewers. DESIGN/SETTING: Descriptive report of a Canadian funding initiative to increase the integration of evidence with action. PARTICIPANTS: Four hundred forty-nine representatives in 41 research, practice, and policy coalitions who responded to a RFP and whose proposals were peer reviewed by a transdisciplinary adjudication panel. INTERVENTION: The funder hosted 6 national meetings and issued a letter of intent (LOI) to foster research, practice, and policy collaborations before issuing a RFP. RESULTS: All provinces and territories in Canada were represented by the coalitions. Funded coalitions were 2.5 times more likely than nonfunded coalitions to submit a LOI. A greater proportion of funded coalitions were exposed to the pre-RFP activities (100%) compared with coalitions that were not funded (68%). Overall research, practice, and policy peer reviewer agreement was low (intraclass correlation 0.12). CONCLUSIONS: There is widespread interest in transdisciplinary collaborations to improve cancer and chronic disease prevention. Engagement in networking and knowledge exchange activities, and feedback from LOIs prior to submission of a final application, may contribute to stronger proposals and subsequent funding success. Future evaluations should examine best practices for transdisciplinary peer review to facilitate funding of proposals that on balance have both scientific rigor and are relevant to the real world.
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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 | 0.166 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".