KAP survey on the risk of Chinese patent medicine use among residents in Baotou City,Inner Mongolia
Objective To investigate the current situation of knowledge,attitude and behavior(KAP)of Chinese patent medicine use risk among residents in Baotou City,and to explore its influencing factors.Methods The paper questionnaire was distributed,and the electronic questionnaire was sent by wechat and email to the residents who were over 18 years old,had normal reading ability or understanding ability and volunteered to participate in the survey.The knowledge,attitude and behavior of residents were analyzed.The optimal scale regression was used to analyze the influence of residents'basic information on KAP.Results A total of 520 questionnaires were sent out,among which 503 were valid,with a recovery rate of 96.73%.The average scores of residents'knowledge,attitude and behavior of drug use were(74.12±21.56),(37.71±9.66)and(58.16±6.71),respectively.The results of optimal scale regression analysis showed that the factors affecting the cognitive scores of Chinese patent medicine of Baotou residents were monthly income,education level,work status and occupation(P＜0.05).The factors affecting the scores of Baotou residents'attitude towards medication education included monthly income,residence,education level,work status and occupation(P＜0.05).The factors affecting the scores of drug use behavior of Baotou residents included age,monthly income,residence,education level,work status and occupation(P＜0.05).Conclusion This study conducte the KAP investigation on the behavioral risk of Chinese patent medicine for residents in Baotou city,and found that there are still risks in the knowledge,attitude and behavior of safe drug use of Chinese patent medicine,for the groups that need to focus on the attention,such as low level of education,older people,it is very necessary to targeted develop education and popular science,standardize the use of Chinese patent medicine in clinical practice,and reduce the health damage and waste of resource caused by unreasonable drug use.
NETL
NSTL
万方数据
