A Web Tool to Help Counter the Spread of Misinformation and Fake News: Pre-Post Study Among Medical Students to Increase Digital Health Literacy
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic was accompanied by the spread of uncontrolled health information and fake news, which also quickly became an infodemic. Emergency communication is a challenge for public health institutions to engage the public during disease outbreaks. Health professionals need a high level of digital health literacy (DHL) to cope with difficulties; therefore, efforts should be made to address this issue starting from undergraduate medical students. OBJECTIVE: The aim of this study was to investigate the DHL skills of Italian medical students and the effectiveness of an informatics course offered by the University of Florence (Italy). This course focuses on assessing the quality of medical information using the "dottoremaeveroche" (DMEVC) web resource offered by the Italian National Federation of Orders of Surgeons and Dentists, and on health information management. METHODS: A pre-post study was conducted at the University of Florence between November and December 2020. First-year medical students participated in a web-based survey before and after attending the informatics course. The DHL level was self-assessed using the eHealth Literacy Scale for Italy (IT-eHEALS) tool and questions about the features and quality of the resources. All responses were rated on a 5-point Likert scale. Change in the perception of skills was assessed using the Wilcoxon test. RESULTS: A total of 341 students participated in the survey at the beginning of the informatics course (women: n=211, 61.9%; mean age 19.8, SD 2.0) and 217 of them (64.2%) completed the survey at the end of the course. At the first assessment, the DHL level was moderate, with a mean total score of the IT-eHEALS of 2.9 (SD 0.9). Students felt confident about finding health-related information on the internet (mean score of 3.4, SD 1.1), whereas they doubted the usefulness of the information they received (mean score of 2.0, SD 1.0). All scores improved significantly in the second assessment. The overall mean score of the IT-eHEALS significantly increased (P<.001) to 4.2 (SD 0.6). The item with the highest score related to recognizing the quality of health information (mean score of 4.5, SD 0.7), whereas confidence in the practical application of the information received remained the lowest (mean of 3.7, SD 1.1) despite improvement. Almost all students (94.5%) valued the DMEVC as an educational tool. CONCLUSIONS: The DMEVC tool was effective in improving medical students' DHL skills. Effective tools and resources such as the DMEVC website should be used in public health communication to facilitate access to validated evidence and understanding of health recommendations.
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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».