Geographic Concentration of RCI-Listed Rehabilitation Training Institutions in India: A Reproducible National Register Analysis
This study analyzes a reconstructed 2026 Rehabilitation Council of India (RCI) register to demonstrate that listed rehabilitation training institutions are geographically concentrated, with Uttar Pradesh and Rajasthan accounting for nearly half of the 1,068 entries, while emphasizing that these counts reflect regulatory listings rather than active training capacity or workforce supply.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
To build a healthy society, a country needs enough skilled workers to help people recover from injury or manage long-term conditions. This is the goal of rehabilitation. But before you can train enough people, you must know where the training happens. Governments keep lists of schools and colleges that are allowed to teach these skills, but a list on paper is not the same as a busy classroom. A name on a roster does not tell you if the school is currently open, how many students it can hold, or if the graduates stay in the area to work. To understand the real landscape of training, researchers need to look at the raw data of these official lists and map exactly where the institutions are located, without guessing about their daily operations.
A recent study by Koti Reddy Saripalli takes a close look at the official register of rehabilitation training institutions in India. The researcher examined a single, fixed document published by the Rehabilitation Council of India, dated February 4, 2026. This document is a 113-page list that contains codes, names, and addresses for every institution recognized by the council at that time. The goal was not to count how many students are enrolled or to judge the quality of the education. Instead, the study simply asked a geographic question: where are these schools, and how are they spread across the country? By treating the list as a snapshot of the nation's training infrastructure, the researcher could see the pattern of distribution without making assumptions about the future or the current activity of each school.
The researcher began by carefully reconstructing the entire list from the original document. Using computer tools to read the text, they extracted every single institution code and its corresponding serial number, ensuring that no entry was missed or duplicated. They checked the state headings on each page to assign every school to its correct location, covering all 36 states and union territories of India. Even if a state had no schools listed, it was kept in the count as a zero, so the map would remain complete. The process involved cross-checking the names and addresses to fix small errors, such as correcting the spelling of state names or ensuring that a school's name was not accidentally mixed with its address. In the end, the study confirmed 1,068 unique institutions, with every serial number from 1 to 1,068 accounted for.
The results revealed a striking pattern of concentration. While schools were present in 32 of the 36 jurisdictions, they were not spread out evenly. Two states, Uttar Pradesh and Rajasthan, together held nearly half of all the listed institutions. Specifically, Uttar Pradesh had 254 entries and Rajasthan had 228, totaling 482 schools. When the five largest states were combined, they accounted for more than 60 percent of the entire national list. The top ten states held almost 80 percent of the entries. This means that the vast majority of the country's recognized training centers are clustered in a small number of regions, while many other states have very few or none at all. Four jurisdictions—Andaman and Nicobar Islands, Dadra and Nagar Haveli and Daman and Diu, Lakshadweep, and Nagaland—did not appear in the source document at all.
To measure this uneven spread, the study used standard statistical tools that describe how concentrated a group is. The analysis showed that the distribution is heavily weighted toward the top states, with a high level of inequality in the number of schools per region. The researcher calculated that if the schools were distributed equally among all the states, the pattern would look very different. Instead, the current reality is that a small number of places hold the bulk of the training capacity. This finding does not mean that the other states are empty of rehabilitation services, nor does it prove that the crowded states have too many schools. It simply maps the official entries as they exist on the government list.
The study is careful to explain what this data cannot tell us. The list shows where institutions are registered, but it does not prove that every school on the list is currently active or accepting students. It does not reveal how many people are actually being trained, where those students come from, or whether they find jobs after graduating. A school might be listed but have no students, or it might be closed for a year. The numbers also do not account for the size of the population in each state; a state with a small population might have a high number of schools relative to its people, while a large state might have fewer. The study explicitly avoids making claims about shortages or surpluses of workers, focusing only on the geographic location of the registered institutions.
This work provides a clear, reproducible foundation for future research. By creating a clean, verified map of where these schools are located, the study gives planners and researchers a reliable starting point. They can now use these codes to gather more detailed information, such as how many students are enrolled, what courses are offered, and where graduates end up working. The study suggests that to truly understand the workforce, one must move beyond the static list and look at the flow of students and the needs of the communities they serve. Until that additional data is collected, the map remains a picture of potential rather than a picture of reality.
The final conclusion is that the training landscape in India is geographically concentrated, with a heavy reliance on a few key states. The study offers a complete and verified record of the institutions as they appeared in early 2026, serving as a tool for further investigation. It highlights that while the framework for rehabilitation training exists across most of the country, the actual density of recognized institutions varies greatly. For anyone looking to improve access to rehabilitation services, the next step is to use this map to ask deeper questions about capacity, demand, and the movement of professionals, ensuring that the training available matches the needs of the people who require it.
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.