Prevalence and Correlates of COVID-19 Vaccine Information on Family Medicine Practices’ Websites in the United States: Cross-sectional Website Content Analysis
Notice bibliographique
Résumé
BACKGROUND: Primary care providers are regarded as trustworthy sources of information about COVID-19 vaccines. Although primary care practices often provide information about common medical and public health topics on their practice websites, little is known about whether they also provide information about COVID-19 vaccines on their practice websites. OBJECTIVE: This study aimed to investigate the prevalence and correlates of COVID-19 vaccine information on family medicine practices' website home pages in the United States. METHODS: We used the Centers for Medicare and Medicaid National Provider Identifier records to create a sampling frame of all family medicine providers based in the United States, from which we constructed a nationally representative random sample of 964 family medicine providers. Between September 20 and October 8, 2021, we manually examined the practice websites of these providers and extracted data on the availability of COVID-19 vaccine information, and we implemented a 10% cross-review quality control measure to resolve discordances in data abstraction. We estimated the prevalence of COVID-19 vaccine information on practice websites and website home pages and used Poisson regression with robust error variances to estimate crude and adjusted prevalence ratios for correlates of COVID-19 vaccine information, including practice size, practice region, university affiliation, and presence of information about seasonal influenza vaccines. Additionally, we performed sensitivity analyses to account for multiple comparisons. RESULTS: Of the 964 included family medicine practices, most (n=509, 52.8%) had ≥10 distinct locations, were unaffiliated with a university (n=838, 87.2%), and mentioned seasonal influenza vaccines on their websites (n=540, 56.1%). In total, 550 (57.1%) practices mentioned COVID-19 vaccines on their practices' website home page, specifically, and 726 (75.3%) mentioned COVID-19 vaccines anywhere on their practice website. As practice size increased, the likelihood of finding COVID-19 vaccine information on the home page increased (n=66, 27.7% among single-location practices, n=114, 52.5% among practices with 2-9 locations, n=66, 56.4% among practices with 10-19 locations, and n=304, 77.6% among practices with 20 or more locations, P<.001 for trend). Compared to clinics in the Northeast, those in the West and Midwest United States had a similar prevalence of COVID-19 vaccine information on website home pages, but clinics in the south had a lower prevalence (adjusted prevalence ratio 0.8, 95% CI 0.7 to 1.0; P=.02). Our results were largely unchanged in sensitivity analyses accounting for multiple comparisons. CONCLUSIONS: Given the ongoing COVID-19 pandemic, primary care practitioners who promote and provide vaccines should strongly consider utilizing their existing practice websites to share COVID-19 vaccine information. These existing platforms have the potential to serve as an extension of providers' influence on established and prospective patients who search the internet for information about COVID-19 vaccines.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».