Geographic trends of scientific output and citation practices in psychiatry
Bibliographic record
Abstract
BACKGROUND: Measures of research productivity are increasingly used to determine how research should be evaluated and funding decisions made. In psychiatry, citation patterns within and between countries are not known, and whether these differ by choice of citation metric. METHOD: In this study, we examined publication characteristics and citation practices in articles published in 50 Web of Science indexed psychiatric and relevant clinical neurosciences journals, between January 2004 and December 2009 comprising 51,072 records that produced 375,962 citations. We compared citation patterns, including self-citations, between countries using standard x(2) tests. RESULTS: We found that most publications came from the USA, with Germany being second and UK third in productivity. USA articles received most citations and the highest citation rate with an average 11.5 citations per article. The UK received the second highest absolute number of citations, but came fourth by citation rate (9.7 citations/article), after the Netherlands (11.4 citations/article) and Canada (9.8 citations/article). Within the USA, Harvard University published most articles and these articles were the most cited, on average 20.0 citations per paper. In Europe, UK institutions published and were cited most often. The Institute of Psychiatry/Kings College London was the leading institution in terms of number of published records and overall citations, while Oxford University had the highest citation rate (18.5 citations/record). There were no differences between the self-citation practices of American and European researchers. Articles that examined some aspect of treatment in psychiatry were the most published. In terms of diagnosis, papers about schizophrenia-spectrum disorders were the most published and the most cited. CONCLUSIONS: We found large differences between and within countries in terms of their research productivity in psychiatry and clinical neuroscience. In addition, the ranking of countries and institutions differed widely by whether productivity was assessed by total research records published, overall citations these received, or citations per paper. The choice of measures of scientific output could be important in determining how research output translates into decisions about resource allocation.
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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.017 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".