Team Science Approach to Developing Consensus on Research Good Practices for Practice‐Based Research Networks: A Case Study
Bibliographic record
Abstract
Using peer learning strategies, seven experienced PBRNs working in collaborative teams articulated procedures for PBRN Research Good Practices (PRGPs). The PRGPs is a PBRN-specific resource to facilitate PBRN management and staff training, to promote adherence to study protocols, and to increase validity and generalizability of study findings. This paper describes the team science processes which culminated in the PRGPs. Skilled facilitators used team science strategies and methods from the Technology of Participation (ToP®), and the Consensus Workshop Method to support teams to codify diverse research expertise in practice-based research. The participatory nature of "sense-making" moved through identifiable stages. Lessons learned include (1) team input into the scope of the final outcome proved vital to project relevance; (2) PBRNs with diverse domains of research expertise contributed broad knowledge on each topic; and (3) ToP® structured facilitation techniques were critical for establishing trust and clarifying the "sense-making" process.
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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 | 0.202 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.006 | 0.007 |
| 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; 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".