Variations in Unmet Health Care Needs by Perceptions of Social Media Health Mis- and Disinformation, Frequency of Social Media Use, Medical Trust, and Medical Care Discrimination: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Unmet need for health care is defined as choosing to postpone or completely avoid necessary medical treatment despite having a need for it, which can worsen current conditions or contribute to new health problems. The emerging infodemic can be a barrier that prevents people from accessing quality health information, contributing to lower levels of seeking medical care when needed. Objective: We evaluated the association between perceptions of health mis- and disinformation on social media and unmet need for health care. In addition, we evaluated mechanisms for this relationship, including frequency of social media use, medical trust, and medical care discrimination. Methods: Data from 3964 active adult social media users responding to the 2022 Health Information National Trends Survey 6 (HINTS 6), a nationally representative survey, were analyzed. The outcome was unmet need for medical care, defined as delaying or not getting the necessary medical care. The predictor variables were perception of social media health mis- and disinformation, frequency of social media use, level of trust in the health care system, and perceived racial and ethnic discrimination when receiving health care. Results: Multivariable logistic regression models indicated that perception of substantial social media health mis- and disinformation (odds ratio [OR] 1.40, 95% CI 1.07-1.82), daily use of social media (OR 1.34, 95% CI 1.01-1.79), low medical trust (OR 1.46, 95% CI 1.06-2.01), and perceived discrimination (OR 2.24, 95% CI 1.44-3.50) were significantly associated with a higher likelihood of unmet need for medical care. Unmet need among adults who did not use social media daily and who did not perceive substantial mis- and disinformation (24%; 95% CI 19%-30%) was lower compared to daily social media users who perceived substantial mis- and disinformation (38%; 95% CI 32%-43%). Adults who perceived substantial mis- and disinformation and had low trust in health care had the highest probability of reporting unmet need (43%; 95% CI 38%-49%) compared to the other three groups. Adults who perceived substantial mis- and disinformation and experienced medical care discrimination had a statistically significant higher probability of reporting unmet need (51%; 95% CI 40%-62%) compared to adults who did not experience medical care discrimination and did not perceive substantial mis- and disinformation (29%; 95% CI 26%-32%). Conclusions: Unmet need for medical care was higher among individuals who perceived a substantial degree of social media mis- and disinformation, especially among those who used social media daily, did not trust the health care system, and experienced racial or ethnic discrimination when receiving health care. To counter the negative effects of social media mis- and disinformation on unmet need for health care, public health messaging must focus on daily social media users as well as improving trust and reducing structural racism in the health care system.
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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 | 0,004 | 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 | 0,001 |
| 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 ».