From Stories to Science: Mapping Global Trends in Narrative Medicine Research (2004–2024)
Bibliographic record
Abstract
Narrative medicine is defined as a medical approach that utilizes the power of stories, both patients' illness narratives and healthcare providers' reflective accounts, to promote healing, foster empathy, and enhance the therapeutic relationship through close attention to the language, metaphor, and meaning embedded in illness experiences. Despite its growing importance in contemporary healthcare, comprehensive bibliometric analyses of narrative medicine research trends remain limited. This study aims to systematically map global research patterns, identify key contributors, and analyze thematic evolution in narrative medicine literature over the past two decades. We conducted a comprehensive bibliometric analysis using the Citexs platform to examine narrative medicine research published from 2004 to 2024. The PubMed database was systematically searched using Boolean search terms: "narrative medicine OR medical storytelling OR clinical narrative OR patient-centered narrative OR healthcare narrative OR personalized medicine narrative". Inclusion criteria encompassed English-language articles only. Publication trends, geographic distribution, institutional productivity, author contributions, and thematic analysis were evaluated using advanced bibliometric techniques and the BioBERT biomedical language representation model for disease entity analysis. A total of 28,029 English-language articles were identified, demonstrating exponential growth with a peak output of 5,063 articles in 2024. The United States led global research productivity with 7,933 articles (28.3%), followed by the United Kingdom (4,704 articles, 16.78%) and Italy (2,743 articles, 9.79%). The University of Toronto emerged as the most productive institution (432 publications). Keyword analysis revealed "systematic review", "COVID-19", "treatment", and "artificial intelligence" as the most frequent terms, indicating the field's responsiveness to contemporary healthcare challenges and technological integration. Disease entity analysis identified "Neoplasms" (5,282 articles), "Death" (4,948 articles), "Pain" (4,345 articles), "Inflammation" (4,338 articles), and "Depressive Disorder" (3,933 articles) as the most commonly studied conditions. This bibliometric analysis demonstrates narrative medicine's transformation from a niche concept to a mainstream healthcare approach with substantial academic recognition. The exponential publication growth reflects increasing institutional support and clinical integration of narrative approaches. Geographic concentration in developed healthcare systems suggests opportunities for global expansion, particularly in culturally diverse contexts. The emergence of artificial intelligence as a research hotspot indicates the field's adaptive capacity to incorporate technological advances while maintaining humanistic principles. The predominance of cancer, death, pain, and mental health conditions underscores narrative medicine's particular relevance in addressing complex psychosocial dimensions of patient care that traditional biomedical approaches cannot fully capture. These findings emphasize narrative medicine's critical role in humanizing modern medical practice and its essential contribution to patient-centered care delivery.
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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 | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.137 | 0.212 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| 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".