Characteristics of Bibliometric Analyses of the Complementary, Alternative, and Integrative Medicine Literature: A Scoping Review
Bibliographic record
Abstract
Introduction: Research on complementary, alternative, and integrative medicine (CAIM) continues to grow. Bibliometric analyses (BAs) are valuable to assess research trends, identify gaps, and understand the evolution of a body of literature, yet there are no systematic or scoping reviews on how these BAs are conducted. This scoping review aimed to systematically review and summarize BAs on CAIM literature to inform and guide future bibliometric studies in this field and beyond. Methods: A scoping review was conducted in accordance with Joanna Briggs Institute guidelines. A systematic search was conducted in MEDLINE, EMBASE, PsycINFO, AMED, CINAHL, Scopus, and Web of Science from database inception to the date of the search on January 5, 2023. Eligible articles were BAs of the CAIM literature. Screening and data extraction were completed independently and in duplicate by at least two reviewers, with findings summarized descriptively. Results: The review included 286 articles published between 1995 and 2023, with approximately 75% published in the last 5 years. Studies were conducted in 36 countries, with China (50%) leading in contributions. All articles used performance analysis techniques, whereas 80% also used science-mapping techniques. The most commonly used performance analysis metrics were “total publications” (98%) and “total citations” (67%). Co-word (63%) and co-authorship (55%) analysis were the most common science mapping techniques. VOSviewer and CiteSpace were the predominant visualization softwares employed. Conclusions: This review demonstrates large methodological diversity in the conduct of CAIM bibliometrics. As a result of this variability, future research should focus on developing uniform methodologies and incorporating diverse metrics and alternative data sources to enhance the reliability and reproducibility of BAs in the CAIM field.
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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 | 0.136 | 0.456 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.181 | 0.206 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".