Education Matters: Certified health professionals have higher credibility than non health professionals on Instagram
Notice bibliographique
Résumé
Social media serves as an accessible source of health information and nutrition information. Instagram, an internationally known social media platform with an average of more than 1 billion monthly active users, allows its users to create and share content. However, the credibility of the nutrition content created by users with unknown qualifications may be questionable. The objective of this study is to assess the credibility of content created by nutrition influencers on Instagram by comparing health professionals with non-health professionals. For this study, “influencer” is defined as an Instagram user with at least 15,000 followers who promotes products, services, or ideas and who creates nutrition- or health-related content. For each influencer (n=29), two posts were selected every month from August 2018 to July 2019. Using the “Credible Information Factsheet” from the Dietitians of Canada, a credibility score based on four dichotomous criteria was created. Looking at the 24 posts of each influencer holistically, a credibility score out of 4 was calculated, with 0 being the least credible and 4 being the most credible. Without exception, a greater proportion of health professionals compared to non-health professionals met each criterion from the “Credible Information Factsheet”. 92% of the health professionals met criteria 1 (Miracle Cure) compared to only 31% of non-health professionals. This demonstrates how the vast majority of health professionals would not promise a miracle cure, while most non-health professionals would readily promise a miracle cure. Additionally, 46% of health professionals met criteria 4 (Research-based) compared to only 19% of non-health professionals, which demonstrates how non-health professionals do not support claims with research. When looking at the total credibility scores for health professionals and non-health professionals, not a single health professional scored a total of 0, while not a single non-health professional scored a total of 4. Most importantly, health professionals had an average credibility score of 2.4, which is twice as high as that of non-health professionals (1.2). Overall, health professionals appeared to be more credible than non-health professionals. By viewing nutrition information posted on Instagram by non-health professionals, followers potentially expose themselves to misinformation. Further research should be undertaken to validate the credibility score based on the “Credible Information Factsheet” by determining how adept the factsheet is at differentiating credibility for Instagram content.
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 | 0,006 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».