Integrating Delphi Consensus Consultation and Community- Based Participatory Research
Bibliographic record
Abstract
Delphi consensus consultation methods and community-based participatory research (CBPR) are distinct approaches that have traditionally been employed separately. This paper explores the integration of Delphi methods with CBPR in a research project that sought to identify effective self-management strategies for bipolar disorder (BD). We introduce our Canadian-based network which specializes in CBPR in BD, and outline the key principles of CBPR approaches. Delphi consensus consultation methods are described and we present the five phases of our Delphi consensus consultation project, conducted within a CBPR framework. Examples of how each project phase incorporated the principles of CBPR are provided, as are personal reflections of community members involved in the project, and broader reflections on challenges commonly encountered in CBPR projects.
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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 | 0.255 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".