Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.2196/23097 http://scihub22266oqcxt.onion/10.2196/23097 33600348!7954112!33600348     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33600348&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Med+Internet+Res 2021 ; 23 (3): e23097 Nephropedia Template TP {{tp|p=33600348|t=2021. Factors Affecting Public Adoption of COVID-19 Prevention and Treatment Information During an Infodemic: Cross-sectional Survey Study |pdf=|usr=}}{{33600348}} gab.com Text Factors Affecting Public Adoption of COVID-19 Prevention and Treatment Information During an Infodemic: Cross-sectional Survey Study JO: J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=33600348 #FOAvip BACKGROUND: With the spread of COVID-19, an infodemic is also emerging. In public health emergencies, the use of information to enable disease prevention and treatment is incredibly important. Although both the information adoption model (IAM) and health belief model (HBM) have their own merits, they only focus on information or public influence factors, respectively, to explain the public's intention to adopt online prevention and treatment information. OBJECTIVE: The aim of this study was to fill this gap by using a combination of the IAM and the HBM as the framework for exploring the influencing factors and paths in public health events that affect the public's adoption of online health information and health behaviors, focusing on both objective and subjective factors. METHODS: We carried out an online survey to collect responses from participants in China (N=501). Structural equation modeling was used to evaluate items, and confirmatory factor analysis was used to calculate construct reliability and validity. The goodness of fit of the model and mediation effects were analyzed. RESULTS: The overall fitness indices for the model developed in this study indicated an acceptable fit. Adoption intention was predicted by information characteristics (beta=.266, P<.001) and perceived usefulness (beta=.565, P<.001), which jointly explained nearly 67% of the adoption intention variance. Information characteristics (beta=.244, P<.001), perceived drawbacks (beta=-.097, P=.002), perceived benefits (beta=.512, P<.001), and self-efficacy (beta=.141, P<.001) jointly determined perceived usefulness and explained about 81% of the variance of perceived usefulness. However, social influence did not have a statistically significant impact on perceived usefulness, and self-efficacy did not significantly influence adoption intention directly. CONCLUSIONS: By integrating IAM and HBM, this study provided the insight and understanding that perceived usefulness and adoption intention of online health information could be influenced by information characteristics, people's perceptions of information drawbacks and benefits, and self-efficacy. Moreover, people also exhibited proactive behavior rather than reactive behavior to adopt information. Thus, we should consider these factors when helping the informed public obtain useful information via two approaches: one is to improve the quality of government-based and other official information, and the other is to improve the public's capacity to obtain information, in order to promote truth and fight rumors. This will, in turn, contribute to saving lives as the pandemic continues to unfold and run its course. Twit Text FOAVip Factors Affecting Public Adoption of COVID-19 Prevention and Treatment Information During an Infodemic: Cross-sectional Survey Study ????J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=33600348 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33600348 #FOAvip English Wikipedia <ref>{{Cite pmid|33600348}}</ref> Factors Affecting Public Adoption of COVID-19 Prevention and Treatment Information During an Infodemic: Cross-sectional Survey Study #MMPMID33600348 Han Y; Jiang B; Guo R J Med Internet Res 2021[Mar]; 23 (3): e23097 PMID33600348show ga BACKGROUND: With the spread of COVID-19, an infodemic is also emerging. In public health emergencies, the use of information to enable disease prevention and treatment is incredibly important. Although both the information adoption model (IAM) and health belief model (HBM) have their own merits, they only focus on information or public influence factors, respectively, to explain the public's intention to adopt online prevention and treatment information. OBJECTIVE: The aim of this study was to fill this gap by using a combination of the IAM and the HBM as the framework for exploring the influencing factors and paths in public health events that affect the public's adoption of online health information and health behaviors, focusing on both objective and subjective factors. METHODS: We carried out an online survey to collect responses from participants in China (N=501). Structural equation modeling was used to evaluate items, and confirmatory factor analysis was used to calculate construct reliability and validity. The goodness of fit of the model and mediation effects were analyzed. RESULTS: The overall fitness indices for the model developed in this study indicated an acceptable fit. Adoption intention was predicted by information characteristics (beta=.266, P<.001) and perceived usefulness (beta=.565, P<.001), which jointly explained nearly 67% of the adoption intention variance. Information characteristics (beta=.244, P<.001), perceived drawbacks (beta=-.097, P=.002), perceived benefits (beta=.512, P<.001), and self-efficacy (beta=.141, P<.001) jointly determined perceived usefulness and explained about 81% of the variance of perceived usefulness. However, social influence did not have a statistically significant impact on perceived usefulness, and self-efficacy did not significantly influence adoption intention directly. CONCLUSIONS: By integrating IAM and HBM, this study provided the insight and understanding that perceived usefulness and adoption intention of online health information could be influenced by information characteristics, people's perceptions of information drawbacks and benefits, and self-efficacy. Moreover, people also exhibited proactive behavior rather than reactive behavior to adopt information. Thus, we should consider these factors when helping the informed public obtain useful information via two approaches: one is to improve the quality of government-based and other official information, and the other is to improve the public's capacity to obtain information, in order to promote truth and fight rumors. This will, in turn, contribute to saving lives as the pandemic continues to unfold and run its course. |Adolescent[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19/epidemiology/*prevention & control/*therapy[MESH] |China/epidemiology[MESH] |Consumer Behavior/*statistics & numerical data[MESH] |Consumer Health Information/*methods/standards/*statistics & numerical data[MESH] |Cross-Sectional Studies[MESH] |Female[MESH] |Health Communication/methods/standards[MESH] |Humans[MESH] |Internet/standards/statistics & numerical data[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Patient Care[MESH] |Reproducibility of Results[MESH] |SARS-CoV-2[MESH] |Surveys and Questionnaires[MESH]Factors Affecting Public Adoption of COVID-19 Prevention and Treatment(epmc).pdf DeepDyve Pubget Overpricing