In contemporary networks, information spread is inherently unequal. Disparities stem from the diverse structural makeups of these networks. Such inequalities manifest in various contexts, including healthcare and employment. While traditional influence maximization algorithms strive to maximize overall information spread, they tend to overlook the notion of fairness in information access, and leave the most vulnerable individuals behind. Addressing the gap in information access is crucial for designing effective public health campaigns and outreach strategies. This paper aims to refine the framework for closing gaps in information access in networks by introducing the concept of spreadability, a new metric for evaluating algorithm performance, and ten new algorithms that aim to minimize the gap between the most and least advantaged nodes in a network. We evaluate the performance of old and new algorithms on a large, comprehensive cross-domain network corpus under several spreadability regimes, and find that none of the existing heuristics are superior to all others. We also discover that under certain conditions, conventional algorithm evaluation accuracy may prove insufficient to gauge the performance of algorithms on large, structurally complex networks. Our findings highlight the importance of network structure in shaping the efficacy of approaches to closing information access gaps, and we believe that observations and tools introduced in this work will be instrumental in designing effective intervention strategies in the future.