Top-Cited Articles In Medical Professionalism: A Bibliometric Analysis versus Altmetric Scores (Preprint)
Bibliographic record
Abstract
BACKGROUND Citation counts of articles have been used by universities and funding bodies to measure scientific outcomes and assess suitability for grant applications. However, citation counts are not without limitations. With the rise of social media, altmetric scores may provide an alternative assessment tool. OBJECTIVE The aims of this study are to assess the characteristics of highly cited articles in medical professionalism and their altmetric scores. METHODS The Web of Science was searched for top-cited articles in medical professionalism, and the characteristics of each article were identified. The altmetric database was searched to identify report for each identified article. A model to assess the relationship between the number of citations and each of key characteristics as well as altmetric scores was developed. RESULTS No correlations were found between the number of citations and number of years since publication (p=0.192), number of institutes (p=0.081), number of authors (p=0.270), females in authorship (p=0.15), or number of grants (p=0.384). The altmetric scores varied from zero to 155, total= 806, median=5.0, (IQR=20). Twitter (54%) and Mendeley (62%) were the most popular altmetric resources. No correlation was found between the number of citations and the altmetric scores (p=0.661). To further assess these variables a model was developed using multivariate analysis; did not show significant differences across subgroups. The topics covered were learning and teaching professionalism, curriculum issues, professional and unprofessional behavior, defining and measuring professionalism. The articles were mainly published in Academic Medicine, Journal of the American Medical Association, Journal of General Internal Medicine, and Annals of Internal Medicine. CONCLUSIONS No correlation was found between citations and any of the article parameters. Altmetric scores of articles were not significantly correlated with citations. Highly cited articles were produced mainly by the United States, Canada, and the United Kingdom. The study reflects the emerging role of altmetric and social media in the dissemination of research. Future studies should investigate the specific features of highly cited articles and factors that reinforce distribution of research data among scholars and non-scholars. CLINICALTRIAL Not applicable.
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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 | 0.017 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.035 | 0.048 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".