Assessing barriers to access and equity for COVID-19 vaccination in the US
Notice bibliographique
Résumé
BACKGROUND: Historical vaccination coverage in economically disadvantaged, ethnic minority, non-affluent white and agricultural populations in the US has lagged coverage in more affluent urban and suburban white populations due to a variety of social and economic factors. In the current COVID-19 pandemic, sociocultural and economic challenges continue to present significant obstacles to achieving equitable uptake of COVID-19 vaccines. The goal of this study was to qualitatively assess perceptions of key US healthcare stakeholders of the most significant barriers to COVID-19 vaccine access and equity to better characterize their expected impact on US communities. METHODS: After conducting a targeted literature review (TLR), we hypothesized 20 high-impact barriers which included structural and logistical barriers, capturing systemic challenges to vaccine accessibility, and attitudinal and informational barriers, affecting patient willingness to pursue vaccination. We developed a qualitative discussion guide, which included both open-ended and closed-ended questions, and interview stimulus material to conduct one-on-one in-depth interviews to assess the expected prevalence, severity, and persistence of these 20 high-impact barriers, which were hypothesized based on TLR. As a part of this qualitative study, we conducted one-on-one in-depth interviews with a diverse set of 15 US healthcare stakeholders who were involved in the COVID-19 vaccine rollout in states with relatively disparate vaccination rates by ethnicity. These stakeholders were selected to reflect an array of roles in the COVID-19 vaccine rollout, including infectious disease specialists, pharmacists, community advocacy representatives, and partners of local governments involved in the COVID-19 vaccine rollout and community education. RESULTS: Respondents identified limited vaccination sites in rural settings and technology-related barriers as the most prevalent and severe structural and logistical barriers in US communities. Respondents assessed COVID-19 vaccine safety concerns and politically motivated skepticism to be the most prevalent and severe attitudinal and informational barriers. Respondents cited proliferation of mobile vaccination clinics and local community messaging to endorse vaccines as the most effective solutions to these top structural and attitudinal barriers. Respondents expected politically motivated skepticism to be the most significant and persistent barrier to broader vaccine uptake in the US. CONCLUSIONS: Our study suggests that attitudinal barriers, particularly politically motivated skepticism, are likely to remain the most persistent challenges to widespread vaccination against COVID-19 in the US.
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 | 0,009 | 0,004 |
| 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 | 0,002 | 0,000 |
| Communication savante | 0,001 | 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 ».