Using the Delphi and Snow Card Techniques to Build Consensus Among Diverse Community and Academic Stakeholders
Bibliographic record
Abstract
BACKGROUND: The New York University- New York City Health and Hospitals Corporation (NYU-HHC) Clinical and Translational Science Institute (CTSI) used a community-based participatory research (CBPR) and consensus-building approach among its community advisory board (CAB) and steering committee (SC) members to formulate research priorities to foster shared research collaborations. METHODS: The Delphi technique is a methodology used to generate consensus from diverse perspectives and organizational agendas through a multi-method, iterative approach to collecting data. A series of on-line surveys was conducted with CAB members to identify health and research priorities from the community perspective. Subsequently, CAB and SC members were brought together and the snow card approach was utilized to narrow to two priority areas for shared research collaborations. RESULTS: Cardiovascular disease (CVD)/obesity and mental health were identified as health disparity areas for shared research collaborations within a social determinants framework. In response, two workgroups were formed with leadership provided by three co-chairs representing the three constituents of the NYU-HHC CTSI: NYU faculty, HHC providers, and community leaders CONCLUSIONS: The Delphi approach fostered ownership and engagement with community partners because it was an iterative process that required stakeholders' input into decision making. The snow card technique allowed for organizing of a large number of discrete ideas. Results have helped to inform the overall CTSI research agenda by defining action steps, and setting an organizing framework to tackle two health disparity areas. The process helped ensure that NYUHHC CTSI research and community engagement strategies are congruent with community priorities.
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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 | 0.293 | 0.229 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| 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".