Evidence on yoga for health: A bibliometric analysis of systematic reviews
Bibliographic record
Abstract
OBJECTIVE: To support the research agenda in yoga for health by comprehensively identifying systematic reviews of yoga for health outcomes and conducting a bibliometric analysis to describe their publication characteristics and topic coverage. METHODS: We searched 7 databases (MEDLINE/PubMed, Embase, PsycINFO, CINAHL, AMED, the Cochrane Database of Systematic Reviews, and PROSPERO) from their inception to November 2019 and 1 database (INDMED) from inception to January 2017. Two authors independently screened each record for inclusion and one author extracted publication characteristics and topics of included reviews. RESULTS: We retrieved 2710 records and included 322 systematic reviews. 157 reviews were exclusively on yoga, and 165 were on yoga as one of a larger class of interventions (e.g., exercise). Most reviews were published in 2012 or later (260/322; 81 %). First/corresponding authors were from 32 different countries; three-quarters were from the USA, Germany, China, Australia, the UK or Canada (240/322; 75 %). Reviews were most frequently published in speciality journals (161/322; 50 %) complementary medicine journals (66/322; 20 %) or systematic review journals (59/322; 18 %). Almost all were present in MEDLINE (296/322; 92 %). Reviews were most often funded by government or non-profits (134/322; 42 %), unfunded (74/322; 23 %), or not explicit about funding (111/322; 34 %). Common health topics were psychiatric/cognitive (n = 56), cancer (n = 39) and musculoskeletal conditions (n = 36). Multiple reviews covered similar topics, particularly depression/anxiety (n = 18), breast cancer (n = 21), and low back pain (n = 16). CONCLUSIONS: Further research should explore the overall quality of reporting and conduct of systematic reviews of yoga, the direction and certainty of specific conclusions, and duplication or gaps in review coverage of topics.
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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 | 0.132 | 0.515 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.250 | 0.275 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.010 |
| 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".