International University Ranking Systems and Their Relevance for the Medical and Health Sciences - A Scoping Review
Bibliographic record
Abstract
Medical and health sciences are disciplines of paramount importance in academia. Universities face a crucial challenge in training qualified health experts for teaching and research in these disciplines. With the globalization of the higher education system, international university ranking systems are an increasingly used tool to assess the excellence of universities and help students and researchers to choose an institution. We conduct a scoping review using Web of Science and Google Scholar to search for scientific literature written in English, published between January 2019 and March 2022. We aim to understand to what extent international university ranking systems are adapted to the disciplines of medical and health sciences. We select any scientific article addressing international university ranking systems and their indicators or proposing a new international university ranking system or new indicators. We include a total of 55 articles. Among them, 10 articles propose a new university ranking system, nine propose a new method to analyze or improve existing international university ranking systems, three propose new indicator(s), and two propose a new database. Almost all articles include an analysis of existing rankings. We find no article that specifically addresses the ranking of schools of medical or health sciences. This scoping review highlights the absence of a specific international university ranking system designed for the disciplines of medical and health sciences. Future researchers could investigate how to develop discipline-specific indicators and promote a university ranking system dedicated to these disciplines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.039 | 0.041 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".