Study on the Self-concept Level of Active Servicemen in Rapidly Advancing High-altitude Cold Areas

BI Ting; FENG Xiaodong; LI Wei; GUO Shuai

doi:10.13730/j.issn.2097-2148.2025.02.012

Abstract：Objective To investigate the status quo of self-concept level of active servicemen in the rapidly advancing high-altitude cold areas.Methods A convenience sampling method was used to select 304 active servicemen who rapidly advancing high-altitude cold areas from November to December 2022 as the research subjects.A self-concept scale was used to investigate their self-concept level and construct a prediction model.Results The total average score of self-concept scale of active servicemen in high-altitude cold areas was(24.89±2.06),among its 6 dimensions,belief and peer self score was(4.65±0.60),social self score was(4.45±0.71),negative self score was(2.22±1.07),military self score was(4.64±0.52),ability and quality self score was(4.57±0.54)and achievement self score was(4.36±0.78).The differences in social self and self-concept among dif-ferent educational levels were statistically significant(P＜0.01);there were statistically significant differences in total assessment and beliefs,peer self and military self among different relationships with parents(P＜0.01);the differences in beliefs and peer self,social self,negative self,military self and self-concept among different family atmospheres were statistically significant(P＜0.05);there was a statistically significant difference in the ability and quality of self among different family economic conditions(P＜0.05).The prediction model was derived as self-concept scale=1.383-0.114 × negative self+0.171 × social self+0.139 × beliefs and peer self+0.098 × military self+0.138 × ability and quality self+0.187 × achievement self.Conclusion Educa-tional level,relationship with parents,family atmosphere and family economic status can significantly affect the self-concept level of active servicemen rapidly advancing high-altitude cold areas.The total score of self-concept scale can be calculated based on the predictive model.