50-year proliferation of music medical science research: A bibliometric review
Bibliographic record
Abstract
The extent to which music research has penetrated the medical science literature relative to other forms of research remains unclear. We sought to explore temporal changes in the number of music related publications relative to all medical literature as well as prespecified research subdomains of drug therapy, alternative therapy, and neuroscience between 1970 and 2019. We conducted a bibliometric review in which we quantified the number of annual publications between 1970 and 2019 using MEDLINE (PUBMED) search engine and mesh terms of “Music”; “Drug therapy”; “Alternative Medicine”; “Neuroscience”. The number of publications were quantified relative to all publications within their corresponding years. We also examined the types of journals, geographical location of publication (Based on corresponding author), and journal impact factors. To ensure appropriate content, we conducted a hand review of a random 400 abstracts to ensure they met appropriate criteria for music-medical research. We used log-linear regression, to test differences in growth rates. We determined that the relative growth in the number of music publications accelerated at a rate higher than all medical related publications or those confined to drug therapy. The proliferation of music research was attributable to higher rates of neuroscience, alternative therapy, and music therapy research. In conclusion, the temporal growth in number of music research publications relative to other comparators over the past 50 years underscores the importance, relevance, and maturation of music as an evolving discipline of contemporary medical science.
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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 | 0.030 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.151 | 0.182 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| 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".