Effectiveness and Related Factors of Narrative Messages in Correcting Health-Related Misinformation: Protocol for a Systematic Review
Notice bibliographique
Résumé
BACKGROUND: The internet and social media have become essential sources of health information for patients and citizens; however, they often disseminate misinformation that lacks scientific evidence. Health-related misinformation can undermine evidence-based treatment, weaken patient-provider relationships, and contribute to adverse health outcomes. Although narratives have been proposed as a promising approach to countering misinformation, their effectiveness remains inconsistent and influenced by various factors. OBJECTIVE: The aim of this study is to assess the effectiveness of narrative messages in correcting health-related misinformation compared to nonnarrative messages. It also seeks to identify message-, sender-, and recipient-related factors that influence the effectiveness of narrative-based corrections. METHODS: This systematic review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Comprehensive searches will be conducted across databases, including PubMed, MEDLINE, CINAHL, PsycINFO, and Web of Science, using keywords related to narratives and correction of health-related misinformation. This review will include quantitative studies evaluating narrative-based corrections for health-related misinformation in experimental and quasi-experimental studies. Studies unrelated to health misinformation or where the full text is unavailable will be excluded. No restrictions on publication year will apply. Only papers written in English will be included. Two independent reviewers will screen the papers using Rayyan QCRI software, with disagreements resolved by a third reviewer. Data extraction will cover health topics (eg, vaccination, tobacco), study characteristics (eg, author, publication year), narrative characteristics (eg, definition of narrative, theoretical foundation), participant characteristics (eg, sociodemographic), methodology (eg, study design, content of interventions and comparators, outcomes and measures, moderating and mediating factors), main results, and discussion. The quality of the eligible studies will be assessed using the Cochrane Risk of Bias 2 tool and the Risk of Bias In Non-randomized Studies - of Interventions tool. RESULTS: The results will be summarized in tables and presented as a descriptive review addressing the effectiveness of narrative corrections in health-related misinformation and the factors influencing their success. The implications of these results for future studies and practices will be elucidated. The findings of this review will be presented at a relevant conference and submitted to a peer-reviewed journal for publication. The aim is to complete the submission process by the northern summer of 2025. CONCLUSIONS: Narrative messages represent a theoretically promising strategy for countering health-related misinformation; however, their effectiveness is context-dependent. This review will offer critical insights into the factors that influence the success of narrative corrections for health-related misinformation, contributing to the development of improved correction strategies and a theoretical understanding of narrative corrections. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69414.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,095 | 0,133 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,007 |
| Méta-épidémiologie (sens large) | 0,025 | 0,022 |
| Bibliométrie | 0,013 | 0,012 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,074 | 0,009 |
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 source (Gemma direct ou Codex distillé), 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 ».