{"title":"基于健康信念模型的预测因子和一线卫生工作者对COVID-19疫苗接种的看法","authors":"Jitendra Majhi, Paramita Sengupta, Preetika Banerjee, Aloke Biswas, Kayur Mehta, Anita Shet, Ninad Vilas Nagrale","doi":"10.6026/973206300211404","DOIUrl":null,"url":null,"abstract":"<p><p>COVID-19 pandemic presented with a scenario wherein the healthcare delivery system was overwhelmed by unprecedented COVID morbidities. Vaccines were made available in a short span of time during the pandemic for the public that required the frontline workers especially the ASHAs to overcome their own hesitancies of the newer vaccine before convincing the community for vaccination. A cross-sectional survey on the perceptions of COVID vaccination was conducted wherein 74 ASHAs participated in the interview involving Likert scale responses on vaccine acceptance which were modelled on Health Belief Model (HBM) to find predictors of vaccine acceptance by performing ordinal regression analysis. It was seen that the domains of 'Benefit', 'Barrier' and 'Modifying Variable' appeared to increase the acceptance of the vaccine whereas the domains of 'Susceptibility' and 'Severity' had decreasing effect on acceptance, of which only 'Severity' domain significantly had a negative effect on COVID vaccine acceptance amongst the frontline workers. HBM is a useful tool which can provide information about the facilitating and negating factors of newer vaccines introduction which can be utilized by public health researchers to better understand the perceptions and accordingly vaccine introduction approaches can be modified.</p>","PeriodicalId":8962,"journal":{"name":"Bioinformation","volume":"21 6","pages":"1404-1415"},"PeriodicalIF":1.9000,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449528/pdf/","citationCount":"0","resultStr":"{\"title\":\"Health belief model based predictors and perceptions of COVID-19 vaccination amongst frontline health workers.\",\"authors\":\"Jitendra Majhi, Paramita Sengupta, Preetika Banerjee, Aloke Biswas, Kayur Mehta, Anita Shet, Ninad Vilas Nagrale\",\"doi\":\"10.6026/973206300211404\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>COVID-19 pandemic presented with a scenario wherein the healthcare delivery system was overwhelmed by unprecedented COVID morbidities. Vaccines were made available in a short span of time during the pandemic for the public that required the frontline workers especially the ASHAs to overcome their own hesitancies of the newer vaccine before convincing the community for vaccination. A cross-sectional survey on the perceptions of COVID vaccination was conducted wherein 74 ASHAs participated in the interview involving Likert scale responses on vaccine acceptance which were modelled on Health Belief Model (HBM) to find predictors of vaccine acceptance by performing ordinal regression analysis. It was seen that the domains of 'Benefit', 'Barrier' and 'Modifying Variable' appeared to increase the acceptance of the vaccine whereas the domains of 'Susceptibility' and 'Severity' had decreasing effect on acceptance, of which only 'Severity' domain significantly had a negative effect on COVID vaccine acceptance amongst the frontline workers. HBM is a useful tool which can provide information about the facilitating and negating factors of newer vaccines introduction which can be utilized by public health researchers to better understand the perceptions and accordingly vaccine introduction approaches can be modified.</p>\",\"PeriodicalId\":8962,\"journal\":{\"name\":\"Bioinformation\",\"volume\":\"21 6\",\"pages\":\"1404-1415\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449528/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.6026/973206300211404\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.6026/973206300211404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Health belief model based predictors and perceptions of COVID-19 vaccination amongst frontline health workers.
COVID-19 pandemic presented with a scenario wherein the healthcare delivery system was overwhelmed by unprecedented COVID morbidities. Vaccines were made available in a short span of time during the pandemic for the public that required the frontline workers especially the ASHAs to overcome their own hesitancies of the newer vaccine before convincing the community for vaccination. A cross-sectional survey on the perceptions of COVID vaccination was conducted wherein 74 ASHAs participated in the interview involving Likert scale responses on vaccine acceptance which were modelled on Health Belief Model (HBM) to find predictors of vaccine acceptance by performing ordinal regression analysis. It was seen that the domains of 'Benefit', 'Barrier' and 'Modifying Variable' appeared to increase the acceptance of the vaccine whereas the domains of 'Susceptibility' and 'Severity' had decreasing effect on acceptance, of which only 'Severity' domain significantly had a negative effect on COVID vaccine acceptance amongst the frontline workers. HBM is a useful tool which can provide information about the facilitating and negating factors of newer vaccines introduction which can be utilized by public health researchers to better understand the perceptions and accordingly vaccine introduction approaches can be modified.