Educational Video Intervention to Improve Health Misinformation Identification on WhatsApp Among Saudi Arabian Population: Pre-Post Intervention Study
Notice bibliographique
Résumé
BACKGROUND: Health misinformation can adversely affect individuals' quality of life and increase the risk of mortality. People often fail to assess the content of messages before sharing them on the internet, increasing the spread of misinformation. The problem is exacerbated by the growing variety of digital information environments, especially social media, which presents as an effective platform for spreading misinformation due to its rapid information-sharing capabilities. Educational interventions have been developed to help consumers verify the validity of digital health information. However, tools designed to detect health misinformation on social media content have not been validated. Given the increased use of social media platforms, particularly WhatsApp, it is crucial to develop tools to help consumers assess the credibility of messages and detect misinformation. OBJECTIVE: The main objective of this study is to develop and assess an educational tool aimed at educating consumers about detecting health misinformation on WhatsApp. The secondary objective is to assess the association between demographic factors and knowledge levels. METHODS: The study used a single-arm, pre-post intervention design to evaluate the effectiveness of an educational video in improving participants' ability to detect health-related misinformation in WhatsApp messages. In the first phase, an educational video intervention was developed and validated. In the second phase, participants were invited to complete a web-based survey that consisted of pre-evaluation questions, followed by the educational video intervention. Subsequently, they were asked to answer the same questions as the postevaluation questions. RESULTS: The web-based survey received 485 responses. The completion rate was 99.6% (n=483). Statistically significant associations existed between knowledge level and age, gender, employment, and region of residence (P<.05). The video intervention did elicit a statistically significant change in the participants' abilities to identify misinformation in WhatsApp messages (z=-6.887; P<.001). Viewing the video was associated with increased knowledge about the following concepts: checking the "forwarded" label (P<.001), looking for spelling and grammatical errors (P<.001), analyzing the facts (P=.03), checking links (P=.002, P=.001), and assessing the photos and videos (P<.001). There was a statistically significant difference in knowledge level before and after the intervention (P<.001). CONCLUSIONS: This study developed and evaluated the effectiveness of an educational video intervention to improve health misinformation identification on WhatsApp among the Saudi Arabian population. The results indicate that educational videos can be valuable tools for improving participants' abilities to identify misinformation. The outcomes of this research can contribute to our understanding of what constitutes an effective tool for enhancing health misinformation awareness. Such interventions may be particularly useful in combating misinformation among Arabic-speaking populations on WhatsApp, which may ultimately improve eHealth literacy. Limiting the prevalence and impact of misinformation allows people to make better-informed health decisions.
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 | 0,009 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,006 |
| 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 | 0,002 |
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 ».