Age Variation Among US Adults’ Social Media Experiences and Beliefs About Who Is Responsible for Reducing Health-Related Falsehoods: Secondary Analysis of a National Survey
Notice bibliographique
Résumé
Background: We live in a digital age where social media has become an essential part of people's lives. It is also one of the leading platforms responsible for spreading health-related falsehoods. This study explores who adults of different age groups perceive as responsible for reducing health-related falsehoods on social media. Objective: Despite growing concern over older adults' exposure to false health information on social media, little research examines their beliefs on how to address the problem. This study examines how the age of US adults is associated with their reported experiences with health-related falsehoods on social media and their beliefs about who should be tasked with reducing such falsehoods. Methods: This study is a secondary analysis of data from the 2022 Health Information National Trends Survey, a nationally representative survey of US adults (18 years and older). Multivariable logistic regressions estimated how a respondent's age was associated with their self-reported social media use, their difficulty to detect health-related falsehoods on social media, their discussion of health information found on social media with medical providers, and their beliefs regarding who should be responsible for reducing health-related falsehoods on social media. Regression estimates were adjusted for respondents' sociodemographic and health characteristics. Results: Daily social media use decreased with respondents' age. Respondents aged 50-64 years (b=0.515, P=.01) and 65-74 years (b=0.697, P=.002) were more likely than respondents aged 18-34 years to report they strongly agree that it is difficult for them to detect health-related falsehoods on social media. Compared to younger adults, older adults (65-74 years: b=0.818, P=.002; 75 years and older: b=1.058, P<.001) were more likely to believe medical providers should be responsible for reducing online falsehoods. Conclusions: In addition to ongoing efforts by social media platforms to detect and remove falsehoods, the findings suggest medical providers should be tasked with discrediting health-related falsehoods on social media for older adults. However, time during the clinical visit is limited. Future research is needed to discover new approaches and tools tailored to older adults to assist with filtering and discrediting health-related falsehoods on social media.
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 | 0,001 |
| 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 | 0,000 | 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 ».