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http://scihub22266oqcxt.onion/ 33691008!7958992!33691008     free     free     free 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       Pac+Symp+Biocomput 2021 ; 26 (ä): 95-106 Nephropedia Template TP {{tp|p=33691008|t=2021. Characterization of Anonymous Physician Perspectives on COVID-19 Using Social Media Data |pdf=|usr=}}{{33691008}} gab.com Text Characterization of Anonymous Physician Perspectives on COVID-19 Using Social Media Data JO: Pac Symp Biocomput http://www.kidney.de/pget.php?&tw=1&pmid=33691008 #FOAvip Physicians' beliefs and attitudes about COVID-19 are important to ascertain because of their central role in providing care to patients during the pandemic. Identifying topics and sentiments discussed by physicians and other healthcare workers can lead to identification of gaps relating to theCOVID-19 pandemic response within the healthcare system. To better understand physicians' perspectives on the COVID-19 response, we extracted Twitter data from a specific user group that allows physicians to stay anonymous while expressing their perspectives about the COVID-19 pandemic. All tweets were in English. We measured most frequent bigrams and trigrams, compared sentiment analysis methods, and compared our findings to a larger Twitter dataset containing general COVID-19 related discourse. We found significant differences between the two datasets for specific topical phrases. No statistically significant difference was found in sentiments between the two datasets, and both trended slightly more positive than negative. Upon comparison to manual sentiment analysis, it was determined that these sentiment analysis methods should be improved to accurately capture sentiments of anonymous physician data. Anonymous physician social media data is a unique source of information that provides important insights into COVID-19 perspectives. Twit Text FOAVip Characterization of Anonymous Physician Perspectives on COVID-19 Using Social Media Data ????Pac Symp Biocomput http://www.kidney.de/pget.php?&tw=1&pmid=33691008 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33691008 #FOAvip English Wikipedia <ref>{{Cite pmid|33691008}}</ref> Characterization of Anonymous Physician Perspectives on COVID-19 Using Social Media Data #MMPMID33691008 Sullivan KJ; Burden M; Keniston A; Banda JM; Hunter LE Pac Symp Biocomput 2021[]; 26 (ä): 95-106 PMID33691008show ga Physicians' beliefs and attitudes about COVID-19 are important to ascertain because of their central role in providing care to patients during the pandemic. Identifying topics and sentiments discussed by physicians and other healthcare workers can lead to identification of gaps relating to theCOVID-19 pandemic response within the healthcare system. To better understand physicians' perspectives on the COVID-19 response, we extracted Twitter data from a specific user group that allows physicians to stay anonymous while expressing their perspectives about the COVID-19 pandemic. All tweets were in English. We measured most frequent bigrams and trigrams, compared sentiment analysis methods, and compared our findings to a larger Twitter dataset containing general COVID-19 related discourse. We found significant differences between the two datasets for specific topical phrases. No statistically significant difference was found in sentiments between the two datasets, and both trended slightly more positive than negative. Upon comparison to manual sentiment analysis, it was determined that these sentiment analysis methods should be improved to accurately capture sentiments of anonymous physician data. Anonymous physician social media data is a unique source of information that provides important insights into COVID-19 perspectives. |*COVID-19[MESH] |*Physicians[MESH] |*Social Media[MESH] |Computational Biology[MESH] |Humans[MESH] |Pandemics[MESH]Characterization of Anonymous Physician Perspectives on COVID-19 Using(epmc).pdf DeepDyve Pubget Overpricing