A Bibliometric Study of Global Trends in Social Medicine Publications on the Web of Science from 2002 to 2021
Bibliographic record
Abstract
Background and Aim: Bibliometric analysis by describing the state of publications and identifying key entities and emerging topics plays an important role in evaluating research. The aim of the paper is to study the global trends of scientific collaboration networks of researchers, organizations and countries and the co-occurrence of words in the field of social medicine in the database of Web of Science. Materials and Methods: The method of investigation is bibliometric. The sample comprises 8494 publications in the area of social medicine between 2002 and 2021 in the Web of science database. The drawing of the scientific collaboration network of researchers, organisations and countries, and the analysis of the words network of co-occurrence, was made using the bibliometric software Vosviewer. Results: The publication process of social medicine documents in the target period is increasing. Research articles had the highest number of documents frequency and review articles received the most citations. The United States had the most published literature in this area, and most authors and organizations were from that country. The degrees of two countries, Canada and Australia, had the most citations per documents, and the five countries of South Africa, Portugal, Pakistan, India, and Iran were emerging players in this field. The network of words co-occurrence of social medicine in three groups was devoted to “preventive research in social medicine”, “social determinants of health” and “healthy lifestyle, nutrition and physical activity”. In terms of temporal occurrence, the five keywords public health, mental health, social medicine, meta-analysis and epidemiology were emerging subjects in the area of social medicine. Conclusion: Understanding impact of non-clinical studies of social medicine on people’s lives has led to an increase in research in this field. In addition to the traditional role of developed countries, some developing countries are also new players in this field and seeking to develop their infrastructure in social medicine.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.089 | 0.180 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".