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Knowledge and Opinions of Healthcare Professionals about Thirdhand Smoke: A Multi-National, Cross-Sectional Study.

Blanca Quispe-CristóbalCristina Lidón-MoyanoJuan Carlos Martín-SánchezHipólito Pérez-MartínÀurea Cartanyà-HuesoÍñigo Cabriada-SáezSonia de Paz-CantosJose M Martínez-SánchezAdrián González-Marrón
Published in: Healthcare (Basel, Switzerland) (2022)
There is scarce evidence on the knowledge and opinions about third-hand smoke (THS) of health care professionals. The main aim of this study was to explore the knowledge and opinions of health care professionals about THS and, secondarily, to explore the factors that are associated with this knowledge. Cross-sectional study using a snowball sample of multi-national health care professionals (n = 233). Data were obtained from an exploratory, online questionnaire. The health care professionals' knowledge and opinions on THS were described with absolute frequency and percentage. Chi-square and Fisher-Freeman-Halton exact tests, and simple logistic regression models, were used to explore the bivariate association between the knowledge of the concept THS and sex, continent of birth, educational level, occupation, years of experience, and attitude towards smoking. Finally, a multivariable logistic regression model incorporating all the above variables was fitted. A total of 65.2% of the participants were unaware of the term THS before the study began. In the bivariate analysis, an association was found between prior knowledge of the term THS and continent of birth ( p -value = 0.030) and occupation ( p -value = 0.014). In the multivariable logistic regression model, a significant association was observed between prior knowledge of the concept THS and sex ( p -value = 0.005), continent of birth ( p -value = 0.012), and occupation ( p -value = 0.001). Almost two out of three health care professionals who participated in our study did not know what THS was. Educational activities on this topic should be implemented.
Keyphrases
  • healthcare
  • cross sectional
  • gestational age
  • health information
  • preterm infants
  • quality improvement
  • deep learning
  • data analysis