Building Timely Consensus Among Diverse Stakeholders: An Adapted Nominal Group Technique
Bibliographic record
Abstract
PURPOSE: Building timely consensus among diverse stakeholders is important in primary health care research. Consensus can be obtained using the nominal group technique which includes 5 steps: (1) introduction and explanation; (2) silent generation of ideas; (3) sharing ideas; (4) discussion; and (5) voting and ranking. The main challenges in using this technique are a lack of representation of different stakeholder opinions and the amount of time taken to reach consensus. In this paper, we demonstrate how to effectively achieve consensus using an adapted nominal group technique that mitigates the challenges. METHODS: This project aimed to reach consensus on the priority care domains for individuals aged 65 or older, using an adapted nominal group technique with 4 strategies: (1) recruit 4 stakeholders groups (older people, clinicians, managers, decision makers) by using maximum variation and snowballing sampling approaches; (2) use remote tools to ensure high participation; (3) add an individual pre-elicitation activity to increase effectiveness; and (4) adapt discussions to the stakeholders' preferences for meaningful engagement. RESULTS: In total, 28 diverse stakeholders participated. After the pre-elicitation activity and 1 round of group discussion, we reached consensus on a priority domain called symptoms, functioning, and quality of care. Adaptive group discussions and remote tools were the most effective strategies. All participants strongly agreed that they were able to express their views freely. Some perceived a need for emphasizing the alignment between the research objectives and anticipated practice and policy implications. CONCLUSIONS: This adapted nominal group technique is an effective and enriching method when timely consensus is needed among diverse stakeholders. Health care researchers in various fields can benefit from using this research methodology.
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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 | 0.123 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".