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10.1007/s40271-020-00494-9

http://scihub22266oqcxt.onion/10.1007/s40271-020-00494-9
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33840078!8035860!33840078
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suck abstract from ncbi


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pmid33840078      Patient 2021 ; 14 (3): 347-358
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  • Public Preferences for Government Response Policies on Outbreak Control #MMPMID33840078
  • Ozdemir S; Tan SNG; Chaudhry I; Malhotra C; Finkelstein EA
  • Patient 2021[May]; 14 (3): 347-358 PMID33840078show ga
  • OBJECTIVE: The aim of this study was to assess the extent to which public support for outbreak containment policies varies with respect to the severity of an infectious disease outbreak. METHODS: A web-enabled survey was administered to 1017 residents of Singapore during the coronavirus disease 2019 (COVID-19) pandemic, and was quota-sampled based on age, sex, and ethnicity. A fractional-factorial design was used to create hypothetical outbreak vignettes characterised by morbidity and fatality rates, and local and global spread of an infectious disease. Each respondent was asked to indicate which response policies (among five policies restricting local movement and four border control policies) they would support in five randomly assigned vignettes. Binomial logistic regressions were used to predict the probabilities of support as a function of outbreak attributes, personal characteristics, and perceived policy effectiveness. RESULTS: Likelihood of support varied across government response policies but was generally higher for border control policies compared with internal policies. The fatality rate was the most important factor for internal policies, while the degree of global spread was the most important for border control policies. In general, individuals who were less healthy, had higher-income, and were older were more likely to support these policies. Perceived effectiveness of a policy was a consistent and positive predictor of public support. CONCLUSIONS: Our findings suggest that campaigns to promote public support should be designed specifically to each policy and tailored to different segments of the population. They should also be adapted based on the evolving conditions of the outbreak in order to receive continued public support.
  • |*Public Opinion[MESH]
  • |Adult[MESH]
  • |Age Factors[MESH]
  • |COVID-19/*epidemiology/mortality/*prevention & control[MESH]
  • |Communicable Disease Control/*organization & administration[MESH]
  • |Emigration and Immigration/legislation & jurisprudence[MESH]
  • |Ethnicity[MESH]
  • |Female[MESH]
  • |Government Programs/*organization & administration[MESH]
  • |Health Status[MESH]
  • |Humans[MESH]
  • |Intensive Care Units/statistics & numerical data[MESH]
  • |Logistic Models[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Pandemics[MESH]
  • |SARS-CoV-2[MESH]
  • |Sex Factors[MESH]
  • |Singapore/epidemiology[MESH]


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