Teaching Workload Inequality and Temporal Patterns in Residency Training: A Gini Coefficient Analysis

Abstract

Introduction: Teaching workload inequality among clinical faculty is recognized but rarely quantified, and the temporal dimension of teaching remains largely unexplored. This study applied the Gini coefficient to quantify teaching workload distribution and examined its association with temporal scheduling patterns.

Methods: We analyzed 3,177 speaker-activity pairs (356 participating faculty, 27 departments) from an electronic teaching platform at the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China (2022–2025). Gini coefficients with bootstrap 95% CIs quantified inequality at hospital-wide and department levels. Temporal associations were assessed using Spearman correlations with Benjamini-Hochberg correction, and partial correlations controlling for teaching volume evaluated independent temporal effects. Annual cross-sectional Gini coefficients and a core cohort analysis distinguished compositional change from within-group redistribution.

Results: The hospital-wide Gini was 0.554 (95% CI: 0.514–0.590); the top 20% delivered 57.8% of activities while the bottom 50% contributed 12.8%. Department-level Gini ranged from 0.000 to 0.702. Teaching methods exhibited temporal signatures (chi-square = 178.1, p < 0.001, Cramer’s V = 0.17). Departments with greater evening teaching had higher inequality bivariately (rho = 0.547, q = 0.027), but the association was not significant after controlling for volume (partial rho = 0.348, p = 0.088). Over four years, Gini declined from 0.531 to 0.439 with faculty expansion from 102 to 269, while a 41-faculty core cohort showed stable inequality (0.445–0.494), indicating compositional change.

Conclusions: Teaching workload among participating faculty was moderately to highly concentrated. The Gini coefficient with temporal analysis offers a practical framework for monitoring workload distribution in residency training programs.