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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 환경독성보건학회 Environmental Analysis Health and Toxicology Environmental Analysis Health and Toxicology Vol.38 No.1
- 발행연도
- 2023.3
- 수록면
- 20 - 27 (8page)
이용수
초록· 키워드
오류제보하기
Health professionals (HPs) can play an important role in influencing the smoking behavior of their patients and the implementation of smoke-free workplace policies. In some countries physicians and dentists may not have a no-smoking policy in place. Breathing in other people’s tobacco smoke (second-hand smokers) increase the risk of smoking related diseases. Environmental Tobacco smoke ETS causes a similar range of diseases to active smoking, including various cancers, heart disease, stroke, and respiratory diseases. Little is known about the smoking-related attitudes and clinical practices of HPs in Indonesia. Evidence suggests that high smoking rates remain among male HPs; however, the risk perceptions and attitudes to smoking among Indonesian HPs have not been investigated using prediction model artificial neural networks. For this reason, we developed and validated an artificial neural network (ANN) to identify HPs with smoking behavior. The study population consisted of 240 HPs, including 108 (45%) physicians, and 132 (55%) dentists, with more female (n=159) than male participants (n=81) for both professions. Participants were randomly divided into two sets, the training (192) and test (48) sets. The input variables included gender, profession (doctor or dentist), knowledge regarding smoking-related diseases and awareness of smoking provided to their patients, smoke-free policy in place at their workplace, and smoking status. ANN was constructed with data from the training and selection sets and validated in the test set. The performance of ANN was simultaneously evaluated by discrimination and calibration. After the training, we completed the process using the test dataset with a multilayer perceptron network, determined by 36 input variables. Our results suggested that our final ANN concurrently had good precision (89%), accuracy (81%), sensitivity (85%), and area under the curve (AUC; 70%). ANN can be used as a promising tool for the prediction of smoking status based on health risk perceptions of HPs in Indonesia.
목차
Abstract
Introduction
Materials and Methods
Results and Discussion
Conclusions
References
참고문헌 (18)
참고문헌 신청
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DiGiacomo, SI, Jazayeri MA, Barua RS, Ambrose JA. Environmental tobacco smoke and cardiovascular disease. Int J Environ Res Public Health 2019;16(1):96. https://doi.org/10.3390/ijerph16010096
Crofton J, Simpson D. Tobacco: A global threat. 1st ed. Swedish Heart Lung Foundation; 2002.
Stead LF, Bergson G, Lancaster T. Physician advice for smoking cessation. Cochrane Database Syst Rev 2008;16(2):CD000165. https://doi.org/10.1002/14651858.CD000165.pub3
Eriksen MP. Best practices for comprehensive tobacco control programs: opportunities for managed care organisations. Tob Control 2000;9(suppl 1):i11-i14. https://doi.org/10.1136/tc.9.suppl_1.i11
DiGiacomo, SI, Jazayeri MA, Barua RS, Ambrose JA. Environmental tobacco smoke and cardiovascular disease. Int J Environ Res Public Health 2019;16(1):96. https://doi.org/10.3390/ijerph16010096
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