RESUMO
BACKGROUND: The COVID-19 vaccine was initially offered to frontline health care workers (HCWs), due to the high risk of contracting COVID-19 through occupational exposure to patients. Low HCW vaccine uptake can impact overall community-level vaccine uptake. This study used the Diffusion of Innovation (DOI) Theory to understand factors related to COVID-19 vaccine uptake in HCWs. METHODS: We surveyed Pennsylvanian HCWs (excluding Philadelphia) from August 2022 to February 2023. Survey questions inquired about demographics, COVID-19 vaccination status, reasons for receiving/declining the COVID-19 vaccine, and sources of information about the vaccine. RESULTS: Participants (n = 3,490) were 85% female, 89% White, and 93% (n = 3,255) reported receiving at least one dose of a COVID-19 vaccine. HCWs were categorized into adopter categories of the DOI Theory: innovators (56%), early adopters (9%), early majority (11%), late majority (7%), and laggards (17%). The major reason that prompted participants to get the vaccine was to protect them against COVID-19 infection (78%), while the major reason for declining the vaccine was due to concern about possible side effects from the vaccine (78%). CONCLUSIONS: We applied the DOI Theory to characterize adopters and identify factors related to COVID-19 vaccine uptake in HCWs. As updated COVID-19 vaccines are approved for the United States market, our findings may be used to improve vaccine education and communication among HCWs to support vaccine uptake.
RESUMO
BACKGROUND: Along with the growth in popularity of electronic cigarette devices (e-cigs), the variety of e-cig liquids (e-liquid) available to users has also grown. Although some studies have published data about the use of flavored e-liquid, there is no standardized way to group flavors, making it difficult to interpret the data and replicate results across studies. The current study describes a method to classify user-reported e-liquid flavors and presents the resulting proportion of users in each flavor group in a large online survey of e-cig users. METHODS: Three thousand seven hundred sixteen participants completed an online survey about their e-cig use and responded to the following open-ended question regarding their use of e-liquid, "What is your favorite flavor and what brand of flavored liquid do you prefer?" Researchers used a 3 step method to determine the flavor attributes present in the e-liquids reported using an online search engine. Once all flavor attributes were identified, researchers used the constant comparative method to group the flavor attributes and delineate how to classify flavors with mixed components (eg, cinnamon Red Hots as a candy not a spice). RESULTS: The resulting classification scheme and proportions of e-liquids in each category were as follows: Tobacco (23.7%), Menthol/mint (14.8%), Fruit (20.3%), Dessert/sweets (20.7%), Alcohol (2.8%), Nuts/spices (2.0%), Candy (2.1%), Coffee/tea (4.3%), Beverage (3.1%), Unflavored (0.4%), and Don't Know/Other (5.8%). CONCLUSION: To better understand the use of flavored e-liquids, standardized methods to classify the flavors could facilitate data interpretation and comparison across studies. This study proposes a method for classifying the characterizing flavors in e-liquids used most commonly by experienced e-cig users. IMPLICATIONS: Current studies on the use of flavored e-liquid have used unclear methods to collect and report information on the use of flavors. This study adds a proposed method for classifying the flavors in the e-liquids used most commonly by experienced e-cig users. With a clear and explicit method for classifying self-reported flavors, future study results may be more easily compared.