Grappling With the Inclusion of Patients and the Public in Consensus Building: A Commentary on Inclusion, Safety, and Accessibility; Comment on "Evaluating Public Participation in a Deliberative Dialogue: A Single Case Study"
Bibliographic record
Abstract
Deliberative dialogue (DD) may be relatively new in health research but has a rich history in fostering public engagement in political issues. Dialogic approaches are future-facing, comprising structured discussions and consensus building activities geared to the collective identification of actionable and contextualized solutions. Relying heavily on a need for co-production and shared leadership, these approaches seek to garner meaningful collaborations between researchers and knowledge users, such as healthcare providers, decision-makers, patients, and the public. In this commentary, we explore some of the challenges, successes, and opportunities arising from public engagement in DD, drawing also upon insights gleaned from our own research, along with the case study presented by Scurr and colleagues. Specifically, we seek to expand discussions related to inclusion, power, and accessibility in DD, highlight the need for scholarship that addresses the epistemic, methodological, and practical aspects of patient and public engagement within dialogic methods, and identify promising practices.
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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 | 0.032 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.066 | 0.068 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".