Deliberative dialogue for co-design, co-implementation and co-evaluation of health-promoting interventions: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Deliberative dialogue (DD) is a participatory research methodology wherein stakeholders with diverse backgrounds, experiences and interests come together to engage in discussions to build consensus for collaborative decision-making. The methodology is increasingly used in health promotion research to develop equitable solutions to complex problems. A review of PubMed-indexed papers alone showed a 9% increase in published DD studies in 2024 from prior years (2020-2023), with most focusing on health promotion and service co-design. Given the increasing emphasis on multistakeholder engagement in research, there is a need to understand how DD has been used as a methodological tool for the co-design, modifications, implementation, evaluation, and knowledge dissemination of health promotion interventions. This scoping study aims to comprehensively understand the application of DD in intervention design to provide a framework to ensure DD is employed with methodological rigour. It will offer valuable insights into how its systematic use can improve the credibility, validity, and trustworthiness of study findings while respecting the principles of participation and knowledge co-production. METHODS: This scoping review follows the Arksey & O'Malley framework. The Arksey & O'Malley framework is designed to map the key concepts, types of evidence, and gaps in research, consisting of five stages: identifying research questions, selecting relevant studies, screening, data charting, and summarizing results. The research team includes decision-makers, researchers, healthcare providers involved in the co-design, co-implementation and co-evaluation of health-promoting interventions, and two patient partners with previous experience in collaborative decision-making. Searches will be performed across multiple databases such as OVID Medline, PsycINFO, PubMed, CINAHL, and Scopus databases. Studies will undergo abstract and full-text screening using Covidence. Covidence is an online platform designed to simplify the process of creating systematic and other in-depth literature reviews (including scoping reviews, rapid reviews, and meta-syntheses), abstract, full-text screening, and extraction of study details, results, and references. A data extraction template has been co-developed building on Guidance for Reporting Involvement of Patient and Public (GRIPP2), which ensures comprehensive reporting of patient and public involvement in research, and the Consolidated Standards of Reporting Trials (CONSORT) checklist facilitates the consistent reporting of methodologies. This data will allow us to understand how DD is used to co-design health interventions. Data extraction will be performed by one reviewer and verified by a second reviewer for consistency. It will then be synthesized to map how DD has been used across various stages of health promotion interventions. ETHICS AND DISSEMINATION: This scoping review does not require ethics approval as it analyzes data from existing research articles. The results will inform the development of guidelines to support methodologically rigorous DD regarding the co-design, co-implementation, and co-evaluation of health-promoting interventions.
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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 | 0.183 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.021 |
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".