WHAT SHAPES THE COMMUNITIES OF LEARNERS IN A MEDICAL SCHOOL

The interactions among students trigger the dissemination of knowledge through the inter-links and ties that help them transfer the information into the learning communities where they belong. These interactions facilitate the distribution of information, enhance the diversity of perspectives and bridges the gap of access to resources. Ties provide more beyond information, they support the social well-being of learners, act as role model and help students manage stress. These connected students and the ties form the different types of networks that shape, influence and mediate their interactions in a tangible way. A positive association between social ties has been reported between social relationships or peer interactions and better performance. However, these findings were reported using traditional descriptive methods that suffered endogeneity, positing a serious threat to the inferences made. Moreover, little is known about how the networks of friendships in a medical school form and what factors derive the community structure.

This study was done to evaluate the factors that shaped the social structure of medical students’ communities with particular emphasis on the role of academic performance and gender differences. The ties considered in this study are the long-term enduring relationships. The analysis compared gender differences in two parallel sections of a medical school studying the same curriculum. Data about age, residence, grades, socioeconomic status were recorded.

To investigate the role of social connections and academic achievement, we ran the nonparametric Spearman correlation coefficient test. The results of the correlation coefficient between the social popularity measures and performance were statistically significant in the male group and insignificant in the female group. This gender variance might point out to a different mechanism of community building and social ties that differs among genders.

To investigate the factors that affect community building in both genders, We used “exponential-family random graph models” (ERGMs) implemented in Statnet R package to model the networks and identify the factors that best predict the emergence of ties.

The male network included 69 nodes and 365 edges; Besides reciprocity, triangle closure, the city of residence, out-degree and in-degree popularity; the academic performance was a significant factor both the GPA and the difference. In a female network, (50 nodes and 176 edges), academic performance was a not significant factor, both the GPA and the difference; while reciprocity, triangle closure, the city of residence, out-degree and in-degree popularity were. The final model in male and female network showed high degree of goodness-of-fit statistics. These results highlight the issue of homophily on performance, as a significant factor in how males build their friendship network in contrast to females. It also emphasizes the need for better inferential models that genuinely capture the network effect on performance before jumping to conclusions using traditional descriptive models that suffer the risk of endogeneity.