Trends of Social Impact of Medical Research Utilization in the Web of Science Database
Bibliographic record
Abstract
The social dimension is crucial when evaluating initiatives and policies requested or promoted by public and private organizations and society. This research aims to investigate the social impact of medical research utilization based on the Web of Science database from 1990 to 2022.The scientometric method and co-word analysis were used to analyze the data. The Web of Science database was used to collect the data. VOSviewer, HistCite, Bibliometrix R package, and Excel software were employed. There has been an upward trend in the publication of scientific articles on the social impact of medical research utilization, peaking in 2021. Most of these records were published between 2017 and 2022, indicating that the significance of the social implications of research reached its zenith during this period, coinciding with the emergence of fourth-generation universities. The United States (U.S.) and Canada have the highest number of scientific articles on this topic. The countries with the highest coefficients of collaboration in this field are the U.S., Canada, England, and Australia. Graham, Legare, Lewis, Strauss, Stabrooks, and Grimshaw authored the most prominent scientific articles. Notably, "Graham" is the most influential author in this field, having garnered 4,846 citations. Key conceptual terms in this field include knowledge translation, public health, healthcare, knowledge management, quality of life, knowledge transfer, quality improvement, science implementation, dissemination, evidence-based medicine, primary care, health politics, medical education, health promotion, social media, social value, and facilitators. This research serves as a roadmap for future researchers interested in social impact assessment. It contributes to advancing research into social impact and medical research utilization.
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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 | 0.013 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.062 | 0.133 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".