the correlation between basnef model constructs, mental health, and severity of premenstrual syndrome in adolescents: a structural equation modeling

Background: premenstrual syndrome (pms) is a common disorder during the reproductive years, emerging in adolescence and more prevalent among high school girls. it can significantly affect academic performance and daily functioning. this study aimed to investigate the correlation between basnef model constructs, mental health, and the severity of premenstrual syndrome in adolescents.methods: this cross-sectional study was conducted in shiraz, iran, in 2025, involving 221 eighth- and ninth-grade female students. data were collected using a demographic questionnaire, a researcher-designed questionnaire based on the basnef model, the depression, anxiety and stress scale - 21 items (dass-21), and the premenstrual symptoms screening tool (psst). data were analyzed using spss version 24 and amos version 24, at a significance level of p<0.05. statistical analyses included pearson's correlation, linear regression, and structural equation modelling (sem).results: pms severity was positively correlated with knowledge (r=0.223, p<0.01) and mental health (r=0.778, p<0.01), and negatively correlated with beliefs (r=-0.318, p<0.01) and enabling factors (r=-0.237, p<0.01). pms severity has no significant correlations with attitudes (r=0.133, p=0.052), and subjective norms (r=-0.031, p=0.646). based on the model derived from sem analysis, mental health, enabling factors, and knowledge had the highest total effects on psst. model fit indices were within the acceptable range: cmin/df=1.776, p=0.052; cfi=0.975; tli=0.937; ifi=0.976; nfi=0.950; rmsea=0.059.conclusions: the findings suggested that all studied variables, particularly mental health, should be considered when designing interventions to reduce pms symptoms in adolescent girls.