Global Trends and Hotspots in Narrative Medicine Studies: A Bibliometric Analysis
Bibliographic record
Abstract
Abstract Background In recent decades, the overemphasis on technical aspects of precision medicine may neglect patients' personal feelings, leading to conflicts and tensions between doctors and patients. Narrative medicine is an interdisciplinary approach aimed at facilitating physician-patient communication, enhancing empathy, and improving the quality of medical care through storytelling. However, the global trends and hotspots in the field of narrative medicine remains unclear. To fill this gap, we conducted a bibliometric analysis of the global scientific publications in the field of narrative medicine, utilizing two visualization tools, CiteSpace and VOSviewer, on papers published in the Web of Science database between 2011–2021.Methods The study presented a bibliometric and visual analysis of the research status, global trends and hotspots in narrative medicine. Using the Web of Science Core Collection (WoSCC) as the data source, 736 articles published between 2011 and 2021 were retrieved and analyzed based on publications, authors, countries, institutions, journals, and keywords.Results Over the past decade, the number of publications in this field has steadily increased each year. The study found that American scholars contributed the most research papers in this field (369 papers) and that the United States is the key node in cooperation with UK, Canada, Italy and other countries. Furthermore, the most influential research team was found to be Columbia University (with 29 papers and 406 citations). The current research hotspots were classified into four clusters: narrative medicine education, consumer health information, health insurance and medical overuse. The function of education and the application of narrative medicine were identified as potential future research hotspots.Conclusion The current study suggested active cooperation among authors, institutions and countries. Among the four clusters, NM education are closely related with the other three clusters. Thus, more attention should be paid to the function of NM education.
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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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.204 | 0.250 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".