Bibliometric analysis of interdisciplinary communication and cooperation in medical field based on Web of Science
Bibliographic record
Abstract
ObjectiveTo analyze development status，research hotspots and research frontiers of interdisciplinary communication and cooperation（ICC） in medical field by using bibliometrics methods.MethodsArticles were retrieved systematically from core collection of documents in the Web of Science database，and distribution characteristics，research hotspots and research frontiers of ICC field were analyzed by CiteSpace software.ResultsA total of 1 309 articles were screened.The United States，Canada and other developed countries，and the institutions and authors from those countries had more research results in the field of ICC.Interdisciplinary collaboration，interdisciplinary communication，interprofessional education，teamwork，awareness and health care were the research hotspots in this field.Interdisciplinary collaboration，interdisciplinary communication，challenge，leadership，and handover were the frontiers of research in this field.Qualitative research and randomized controlled trials were research method often used in this field.ConclusionsThe United States and other developed countries have more research results in the field of ICC.China is in the stage of exploration and development，and the number of studies is relatively small.Researchers can combine Chinese cultural background in medical field to strengthen international exchanges and cooperation，so as to promote Chinese ICC research development.
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 | 0.019 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.236 | 0.249 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".