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10.1016/j.dsx.2021.05.006

http://scihub22266oqcxt.onion/10.1016/j.dsx.2021.05.006
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34000711!8105307!34000711
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suck abstract from ncbi


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pmid34000711      Diabetes+Metab+Syndr 2021 ; 15 (3): 1017-1022
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  • Using structural equation modeling to predict Indian people s attitudes and intentions towards COVID-19 vaccination #MMPMID34000711
  • Mir HH; Parveen S; Mullick NH; Nabi S
  • Diabetes Metab Syndr 2021[May]; 15 (3): 1017-1022 PMID34000711show ga
  • BACKGROUND AND AIM: Understanding people's attitudes towards Covid-19 vaccination is crucial to the successful implementation of a vaccination program. Hence this research study seeks to identify critical factors influencing Indian people's attitudes and intentions to take up Covid-19 vaccinations. METHODS: An online questionnaire was administered to a sample (n = 254) drawn from Indian population, to assess the impact of perceived benefits, risk perceptions, social media exposure, social norms, and trust associated with Covid-19 vaccines on people's attitudes towards Covid-19 vaccines and their intentions to take up the Covid-19 vaccinations. RESULTS: The findings showed that the perceived benefits, social norms, and trust correlated significantly with people's attitudes towards Covid-19 vaccinations. In contrast, risk perceptions and social media exposure showed an insignificant influence on people's attitudes towards Covid-19 vaccinations. Social norms, trust, and people's attitudes towards the Covid-19 vaccinations are significantly correlated with their intentions to take up Covid-19 vaccinations. On the contrary, social media exposure was found to have an insignificant influence on people's intentions to take up Covid-19 vaccinations. CONCLUSION: Participants' intentions to take up Covid-19 vaccinations was influenced mainly by their attitudes and perceptions of Covid-19 vaccines in general, which strongly confirms the importance of various dimensions (perceived benefits, trust, social norms) of Covid-19 vaccines in cultivating Covid-19 vaccination acceptance among participants'.
  • |*Health Knowledge, Attitudes, Practice[MESH]
  • |*Intention[MESH]
  • |Adolescent[MESH]
  • |Adult[MESH]
  • |Aged[MESH]
  • |Aged, 80 and over[MESH]
  • |Attitude to Health[MESH]
  • |COVID-19 Vaccines/*therapeutic use[MESH]
  • |COVID-19/*prevention & control/psychology[MESH]
  • |Female[MESH]
  • |Health Literacy/statistics & numerical data[MESH]
  • |Humans[MESH]
  • |India/epidemiology[MESH]
  • |Internet[MESH]
  • |Latent Class Analysis[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Models, Theoretical[MESH]
  • |Patient Acceptance of Health Care/psychology/statistics & numerical data[MESH]
  • |SARS-CoV-2/immunology[MESH]
  • |Surveys and Questionnaires[MESH]
  • |Vaccination/*psychology/statistics & numerical data[MESH]


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