Characteristics of bibliometric analyses of the complementary, alternative, and integrative medicine literature: A scoping review protocol
Bibliographic record
Abstract
Background: There is a growing body of literature on complementary, alternative, and integrative medicine (CAIM), which offers a holistic approach to health and the maintenance of social and cultural values. Bibliometric analyses are an increasingly commonly used method employing quantitative statistical techniques to understand trends in a particular scientific field. The objective of this scoping review is to investigate the quantity and characteristics of evidence in relation to bibliometric analyses of CAIM literature. Methods: The following bibliographic databases will be searched: MEDLINE, EMBASE, PsycINFO, AMED, CINAHL, Scopus and Web of Science. Studies published in English, conducting any type of bibliometric analysis involving any CAIM therapies, as detailed by an operational definition of CAIM adopted by Cochrane Complementary Medicine, will be included. Conference abstracts and study protocols will be excluded. The following variables will be extracted from included studies: title, author, year, country, study objective, type of CAIM, health condition targeted, databases searched in the bibliometric analysis, the type of bibliometric variables assessed, how bibliometric information was reported, main findings, conclusions, and limitations. Findings will be summarized narratively, as well as in tabular and graphical format. Conclusions: To the best of our knowledge, this scoping review will be the first to investigate the characteristics of evidence in relation to bibliometric analyses on CAIM literature. The findings of this review may be useful to identify variations in the objectives, methods, and results of bibliometric analyses of CAIM research literature.
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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 | 0.226 | 0.405 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.058 | 0.061 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.069 | 0.016 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".