Motivation and socialization during summer predict medical students' success: An artificial intelligence study

Purpose: The latest reform of French medical studies has moved the National Ranking Examination before residency to the beginning of the sixth-year for undergraduate medical students, thus placing unprecedented workload during the preceding summer. The main objective was to determine whether study conditions and psychosocial factors were associated with student success in this model of intense workload.

Materials and methods: An online survey designed with six student-partners was sent at a French Medical School after the examination in 2023. The primary outcome was student success in achieving their main goal (Ranking, Knowledge, Well-being). A machine-learning model (eXtreme Gradient Boosting) was developed and explained using Artificial Intelligence. An AI-guided multivariate logistic regression was performed, Odd Ratios were calculated.

Results: Out of 123 responses, 75 (61%) of the students achieved their main goal. Motivation and socialization during the summer were the two most important variables for predicting student success. In guided multivariate logistic regression, summer motivation (Odd Ratio = 4.12, 95%CI[1.75-10.30]), summer loneliness (Odd Ratio = 0.35, 95%CI[0.14-0.86]), and student's main goal (Ranking, Odd Ratio = 2.94, 95%CI[1.15-7.79]) were associated with student success.

Conclusions: Motivation and socialization during the summer preceding high-stakes examinations are strongly predictive of undergraduate medical students' success. This study highlights the importance of well-being during summer for student success.[Box: see text].