LatiNET, a Multilevel Social Network Model to Examine and Address SARS-CoV-2 Misinformation in Low-Income Latinx Communities.

LatiNET,一种多层次社交网络模型,用于检查和解决低收入拉丁裔社区中的 SARS-CoV-2 错误信息。

基本信息

项目摘要

PROJECT SUMMARY/ABSTRACT LatiNET will use a multilevel social network model to examine how SARS-CoV-2 misinformation and Conspiracy Theory (CT) messages are shared across five settings (friends, family, work, health service and influencers), impacting Latinx vaccine hesitancy. Social networks are self-organizing social systems that create and reinforce perceptions, both positive and negative. An important gap in current knowledge relates to the content, context and communication direction about SARS-CoV-2 misinformation and CT messages. By learning how Latinx social network structures hinder or promote SARS-CoV-2 misinformation and CT messages, we will inform the design of interventions that will reduce mistrust/fear and provide correct, timely, and comprehensive information, through multiple social network sources, enabling Latinx to make the best health decisions for themselves and their families. LatiNET will focus on low-income Latinx, which have long struggled with social, economic and health inequalities. Miami-Dade County, Florida will be the site for this study, where almost 100% of residents from in wealthiest areas have received at least one SARS-CoV-2 vaccine dose while fewer than a third of residents in poorer communities, mainly inhabited by Latinx individuals, have been vaccinated.1 We have also identified that misinformation and CT messages are prevalent in Florida.2 We will use Dr. Kanamori’s (PI) K99/R00 social network approaches3-8 and Drs. Uscinski’s and Stoler’s (Co-Is) misinformation and CT message framework2,9-11 to identify how network structures and dynamics introduce and spread misinformation and CT messages that could then influence Latinx vaccine hesitancy. We will also identify network structures and dynamics that promote discussion against SARS-CoV-2 misinformation and CT messages. LatiNET will study: 1) participants’ characteristics, 2) 624 friendship sociocentric networks, 3) 1,872 egocentric networks (family, work and health service), and 4) influencer networks, all of which will be part of our adapted NIMHD framework.12 Our AIMS are: 1) Determine how network structures and dynamics inside Latinx friendship networks shape the spread and adoption of misinformation and CT messages associated with SARS-CoV-2 vaccine hesitancy. 2) Distinguish homophily and dyadic characteristics and dynamics associated with misinformation and CT messages shared with family members, co-workers and health service providers. 3) Identify Latinx affiliations with community, celebrity, public health, political influencer and communication channels that spread CT and anti-CT messages. In all AIMS, we will also study the underlying social and structural factors associated with Latinx health decision-making (e.g., discrimination, stigma, intimate partner violence) and beliefs and behaviors tied to misinformation and CT messages (e.g., individual-level political, psychological, and social factors). LatiNET will provide new information that can inform policy and the design of future interventions to reduce the impact of misinformation and CT messages on SARS-CoV-2 vaccine hesitancy nationwide, and also with different priority populations.
项目总结/文摘

项目成果

期刊论文数量(0)
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Mariano Juan Kanamori Nishimura其他文献

Mariano Juan Kanamori Nishimura的其他文献

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{{ truncateString('Mariano Juan Kanamori Nishimura', 18)}}的其他基金

ÚNETE: Combining Friendship Support Networks and Targeted Messaging from Celebrity Influencers to Reduce Latinx Substance Use Disparities
�NETE:结合友谊支持网络和名人影响者的有针对性的消息来减少拉丁裔药物使用差异
  • 批准号:
    10740012
  • 财政年份:
    2023
  • 资助金额:
    $ 77.78万
  • 项目类别:
FINISHING HIV: An EHE model for Latinos Integrating One-Stop-Shop PrEP Services, a Social Network Support Program and a National Pharmacy Chain
完成艾滋病毒:针对拉丁裔的 EHE 模式,整合一站式 PrEP 服务、社交网络支持计划和全国药房连锁店
  • 批准号:
    10652529
  • 财政年份:
    2022
  • 资助金额:
    $ 77.78万
  • 项目类别:
FINISHING HIV: An EHE model for Latinos Integrating One-Stop-Shop PrEP Services, a Social Network Support Program and a National Pharmacy Chain
完成艾滋病毒:针对拉丁裔的 EHE 模式,整合一站式 PrEP 服务、社交网络支持计划和全国药房连锁店
  • 批准号:
    10459704
  • 财政年份:
    2022
  • 资助金额:
    $ 77.78万
  • 项目类别:
LatiNET, a Multilevel Social Network Model to Examine and Address SARS-CoV-2 Misinformation in Low-Income Latinx Communities.
LatiNET,一种多层次社交网络模型,用于检查和解决低收入拉丁裔社区中的 SARS-CoV-2 错误信息。
  • 批准号:
    10707207
  • 财政年份:
    2022
  • 资助金额:
    $ 77.78万
  • 项目类别:
PrEParados: A Multi-Level Social Network Model to Increase PrEP Enrollment by Latino MSM Self-Identified as Gay, Bisexual
PrEParados:一种多层次社交网络模型,可提高自认是同性恋、双性恋的拉丁裔 MSM 的 PrEP 注册率
  • 批准号:
    10161442
  • 财政年份:
    2020
  • 资助金额:
    $ 77.78万
  • 项目类别:
PrEParados: A Multi-Level Social Network Model to Increase PrEP Enrollment by Latino MSM Self-Identified as Gay, Bisexual
PrEParados:一种多层次社交网络模型,可提高自认是同性恋、双性恋的拉丁裔 MSM 的 PrEP 注册率
  • 批准号:
    10310530
  • 财政年份:
    2020
  • 资助金额:
    $ 77.78万
  • 项目类别:
PrEParados: A Multi-Level Social Network Model to Increase PrEP Enrollment by Latino MSM Self-Identified as Gay, Bisexual
PrEParados:一种多层次社交网络模型,可提高自认是同性恋、双性恋的拉丁裔 MSM 的 PrEP 注册率
  • 批准号:
    10517510
  • 财政年份:
    2020
  • 资助金额:
    $ 77.78万
  • 项目类别:
PrEParados: A Multi-Level Social Network Model to Increase PrEP Enrollment by Latino MSM Self-Identified as Gay, Bisexual
PrEParados:一种多层次社交网络模型,可提高自认是同性恋、双性恋的拉丁裔 MSM 的 PrEP 注册率
  • 批准号:
    10738838
  • 财政年份:
    2020
  • 资助金额:
    $ 77.78万
  • 项目类别:
Multilevel approaches for embracing dyadic, egocentric and two-mode networks which address substance use disorders and HIV risk in Latina seasonal workers
采用二元、自我中心和两种模式网络的多层次方法,解决拉丁季节性工人的药物滥用障碍和艾滋病毒风险
  • 批准号:
    9594629
  • 财政年份:
    2018
  • 资助金额:
    $ 77.78万
  • 项目类别:
Multilevel approaches for embracing dyadic, egocentric and two-mode networks which address substance use disorders and HIV risk in Latina seasonal workers
采用二元、自我中心和两种模式网络的多层次方法,解决拉丁季节性工人的药物滥用障碍和艾滋病毒风险
  • 批准号:
    9203911
  • 财政年份:
    2016
  • 资助金额:
    $ 77.78万
  • 项目类别:

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