Practice‐based Research Network Research Good Practices (PRGPs): Summary of Recommendations
Bibliographic record
Abstract
INTRODUCTION: Practice-based research networks (PBRNs) conduct research in community settings, which poses quality control challenges to the integrity of research, such as study implementation and data collection. A foundation for improving research processes within PBRNs is needed to ensure research integrity. METHODS: Network directors and coordinators from seven U.S.-based PBRNs worked with a professional team facilitator during semiannual in-person meetings and monthly conference calls to produce content for a compendium of recommended research practices specific to the context of PBRNs. Participants were assigned to contribute content congruent with their expertise. Feedback on the draft document was obtained from attendees at the preconference workshop at the annual PBRN meeting in 2013. A revised document was circulated to additional PBRN peers prior to finalization. RESULTS: The PBRN Research Good Practices (PRGPs) document is organized into four chapters: (1) Building PBRN Infrastructure; (2) Study Development and Implementation; (3) Data Management, and (4) Dissemination Policies. Each chapter contains an introduction, detailed procedures for each section, and example resources with information links. CONCLUSION: 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.
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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 | 0.249 | 0.387 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".