Mapping gender measurement approaches through semantic embedding and intersectionality framework reveals hidden trends

In the world of medicine, doctors have long relied on a simple biological distinction to understand health: sex. This refers to the physical traits a person is born with, such as chromosomes and hormones. However, a separate but equally powerful force shapes how people experience illness and recovery: gender. Gender is not a biological fact but a social one, built from the roles, expectations, and daily lives that society assigns to people based on how they are perceived. A person's gender influences their stress levels, their job, who cares for them when they are sick, and how they interact with the world. For decades, medical research often missed this nuance, treating gender as a simple choice between male and female, or ignoring it entirely. This oversight meant that symptoms and treatments specific to a person's social experience were frequently mislabeled as biological differences or left unexplained.

To fix this, researchers have spent years creating tools, or instruments, to measure gender more accurately. These tools are essentially checklists of questions about a person's life, ranging from their occupation and income to their personality traits and caregiving duties. The goal is to turn these complex social experiences into numbers that can be studied alongside biological data. But as the number of these tools grew, a new problem emerged. With dozens of different checklists in use, no one knew how they actually related to one another. Did two different tools measure the same thing? Did they cover the full range of human experience, or did they all focus on the same narrow slice of life? Without a way to compare them, the field risked becoming a collection of disconnected studies that could not build upon each other.

A team of researchers from Germany and the Netherlands set out to map this landscape. They gathered twenty-eight different gender-measurement tools published between 1974 and 2025 and analyzed them not just by reading the questions, but by using a computer method that understands the meaning behind words. Imagine a librarian who can instantly sort thousands of books not just by their title, but by the actual story inside them, grouping together books that tell similar tales even if their covers look different. The researchers used a similar digital approach to see how these twenty-eight tools overlapped, where they diverged, and what blind spots they shared.

The first thing the team discovered was that these tools are far from uniform. They found that the questions asked in these instruments are heavily concentrated in just a few areas of life. Most of the variables measured fall into four main categories: personality and lifestyle, economic status, mental health, and the work of caring for others. Together, these four areas make up nearly eighty percent of all the questions asked across the twenty-eight tools. In contrast, other vital aspects of a person's identity were largely ignored. Questions about age, sexual identity, race, religion, or personal worldviews appeared in very few tools, and often only as an afterthought. This suggests that current medical research is capturing a specific, narrow version of gender, one that fits well with life in wealthy Western nations but might fail to describe the realities of people in other parts of the world or those with different life structures.

When the researchers looked closer at the specific words used in the questions, they found a surprising lack of agreement. Out of nearly four hundred unique variables, only twenty-four appeared in more than one tool. Even then, the wording was often different; one tool might ask about "marital status" while another asks about "civil status," even though they mean the same thing. This fragmentation made it difficult to compare studies directly. To solve this, the team used a computer system that could understand the meaning of these different phrases. They treated the questions like sentences and calculated how similar their meanings were, allowing them to group the tools based on what they were actually trying to measure, rather than just how they were labeled.

This deeper analysis revealed that the tools naturally sorted themselves into six distinct groups. One group focused entirely on how people express their gender identity, asking about self-categorization and social presentation. Another group was dedicated to personality traits, using lists of adjectives to describe masculine or feminine characteristics. A third group looked strictly at social roles, such as income, education, and who does the housework. Other groups mixed these elements, creating tools that combined psychological traits with social factors, or tools that focused specifically on health outcomes like trauma or chronic illness. The researchers found that tools within the same group were very similar to each other, but tools from different groups were often measuring completely different things. For instance, a tool designed to measure personality traits was not interchangeable with one designed to measure economic status, even if both were labeled as "gender measures."

The study also highlighted a significant geographic bias. Almost all the tools came from Europe and North America, specifically from high-income countries. This means the questions asked reflect the social structures of those regions, such as formal employment and specific types of family arrangements. The researchers noted that these tools would likely not work in places where labor markets, household compositions, or ways of measuring income are different. Furthermore, they found that very few of these tools were designed to be used in routine hospital settings. Most required data that doctors do not typically collect, such as detailed surveys about a patient's childhood or specific social habits, making it hard to apply these insights to everyday medical care.

Perhaps the most important finding was that the field has been operating without a shared language. The researchers showed that while many tools exist, they do not form a single, cohesive system. Instead, they are scattered clusters of different approaches, each with its own focus and blind spots. By mapping these relationships, the team provided the first clear picture of how these instruments converge and diverge. They demonstrated that to move forward, the medical community needs to stop treating gender as a single checkbox and start recognizing it as a complex, multi-dimensional experience. The work suggests that before doctors can reliably use gender scores in the clinic, the field must agree on a richer, more diverse vocabulary that captures the full spectrum of human life, rather than just the parts that fit neatly into a binary box. This mapping is not a final solution, but a necessary first step toward building a shared understanding that can finally help medicine see the whole person.