Deliberative dialogue for co-design, co-implementation and co-evaluation of health-promoting interventions: a scoping review protocol
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,152 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».