Comparing alternative and traditional dissemination metrics in medical education
Bibliographic record
Abstract
CONTEXT: The impact of academic scholarship has traditionally been measured using citation-based metrics. However, citations may not be the only measure of impact. In recent years, other platforms (e.g. Twitter) have provided new tools for promoting scholarship to both academic and non-academic audiences. Alternative metrics (altmetrics) can capture non-traditional dissemination data such as attention generated on social media platforms. OBJECTIVES: The aims of this exploratory study were to characterise the relationships among altmetrics, access counts and citations in an international and pre-eminent medical education journal, and to clarify the roles of these metrics in assessing the impact of medical education academic scholarship. METHODS: A database study was performed (September 2015) for all papers published in Medical Education in 2012 (n = 236) and 2013 (n = 246). Citation, altmetric and access (HTML views and PDF downloads) data were obtained from Scopus, the Altmetric Bookmarklet tool and the journal Medical Education, respectively. Pearson coefficients (r-values) between metrics of interest were then determined. RESULTS: Twitter and Mendeley (an academic bibliography tool) were the only altmetric-tracked platforms frequently (> 50%) utilised in the dissemination of articles. Altmetric scores (composite measures of all online attention) were driven by Twitter mentions. For short and full-length articles in 2012 and 2013, both access counts and citation counts were most strongly correlated with one another, as well as with Mendeley downloads. By comparison, Twitter metrics and altmetric scores demonstrated weak to moderate correlations with both access and citation counts. CONCLUSIONS: Whereas most altmetrics showed limited correlations with readership (access counts) and impact (citations), Mendeley downloads correlated strongly with both readership and impact indices for articles published in the journal Medical Education and may therefore have potential use that is complementary to that of citations in assessment of the impact of medical education scholarship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.032 | 0.046 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| 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".