<p dir="ltr">The desire for more compact, efficient, and quieter powertrains in the Electrification Age has once again brought gears into the spotlight. In the field of modern gear design and analysis, loaded tooth contact analysis (LTCA) is one of the most critical simulations performed. Gear stiffness plays a fundamental role in this simulation and is calculated slightly differently in almost every software on the market. The need to accurately resolve gear stiffness to capture pitting, bending, scuffing, efficiency, and noise characteristics of a gear pair makes the topic very relevant to the entire gear industry. This work begins by categorizing all key gear mesh stiffness methodologies and workflows used in both industry and academia, providing the key references from which each methodology originated. The advantages and disadvantages of each gear mesh stiffness methodology are detailed, along with the methods used in the most common gear software on the market. Recommendations are also provided for gear stiffness methodologies to be used in various scenarios. One of the core objectives of this work is to enhance the fidelity of the computationally efficient coupled-slice method through improved theory. In industry, the pure finite element method is too slow on its own to model gear contact efficiently, which is why computationally efficient methods, such as the coupled-slice method, have been developed. The motivation behind the creation of the high-fidelity coupled-slice method is to address the gaps identified in the literature, increase fidelity closer to that of the pure finite element method, while still maintaining fast computation times. Finally, the last core objective of this work focuses on creating a comprehensive validation data set for not one, but six different load cases that a gear LTCA algorithm could encounter. This effort was performed with a single tooth, utilizing a highly refined computationally inefficient pure finite element method, which is the standard technique for validating lower-fidelity methods.</p>