The performance of bibliometric analyses in the health sciences
Bibliographic record
Abstract
A bibliometric analysis (BA) is a knowledge synthesis methodology aimed at quantitively summarizing large amounts of bibliometric data. We aimed to summarize the performance of BAs in the health sciences. We searched Scopus for BAs in the health sciences published prior to May 10, 2023. All identified studies were included. We performed a BA on these studies in two steps: performance analysis and science mapping. For the performance analysis, various indicators of scientific production were calculated using the bibliometrix R package. For the science mapping, VOSviewer was used to generate a co-authorship network and a keyword co-occurrence network. In total, 5,828 BAs were analyzed. Scientific production has exploded in the last years, with more than 1,500 BAs published in 2022 alone. Scientific impact (i.e. citations) has also been rising, although at a lesser pace. The mean number of citations per year per BA was 1.78. China was the most productive country, publishing more BAs than the nine other most productive countries combined. China paradoxically had a lower number of citations per publication compared with the nine other most productive countries. International collaborations were rare. Common BA themes included oncology, public health, neurosciences, mental health, artificial intelligence, and COVID-19. BAs are increasingly common in the health sciences, but their performance remains limited. More international collaborations and standardized guidelines could help improve their performance, notably the frequency at which they are cited.
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 | 0.203 | 0.510 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.178 | 0.230 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".