Analysis on the results of theoretical testing on common parasitic diseases diagnosis: national technique competition of parasitic disease diagnosis in 2013
Objective To understand the current theoretical level on basic knowledge of parasitic disease diagnosis among technicians from disease control and prevention institutions.Methods A national competition on basic knowledge of parasitic disease diagnosis was organized in June,2011.124 technicians participated from disease control and prevention institutions at province,prefecture or county level in 31 provinces.Database was constructed with the essential information of participants and scores of testing results.Appropriate analytical methods were used to analyze the scores by gender,age,professional title,institutions and places of participants by SPSS 17.0 software.Results The score of the 124 competitors ranged from 44 to 98.5 with an average of (83.5±12.1) and the passing rate reached 91.9％.There were no significant differences among genders,and age groups.However,there was significant difference among institutions (H=7.995,P＜0.05) and professional titles(H=13.349,P＜0.05),and the score in senior and intermediate competitors was higher than that in junior competitors (Z=2.232,P＜0.05;Z=3.281,P＜0.05).The score in competitors from southern provinces was higher than that in those from northern provinces (Z=3.279,P＜0.05).The score was higher in the technicians from provinces with control activities in schistosomiasis and malaria than those from provinces without these control activities (Z=3.623,P＜0.05;Z=4.887,P＜0.05).The score in 2013 was higher than the score in 2011 and 2012 (Z=9.239,P＜0.05;Z=5.362,P＜0.05).Conclusion The overall level of theoretical knowledge on basic knowledge of parasitic disease diagnosis is significantly improved owing to national technique competition held for parasitic disease diagnosis in last three years.However,some problems still exist such as the uneven geographical distribution and inadequate coverage,that needs to be improved.
NETL
NSTL
万方数据
维普
