Determining Impact for Anatomical Sciences Education Articles in the Age of Altmetrics
Bibliographic record
Abstract
The impact of academic scholarship has been traditionally measured using citation‐based metrics. In recent years, new platforms (such as social media tools like Twitter and Facebook, and bibliography tools like Mendeley) have become available to allow for dissemination of scholarly papers to both academic and non‐academic audiences. Alternative or article‐level metrics (altmetrics) related to paper's visibility on these platforms, as well as the Altmetric Score (ALT Score, a global index representing attention generated across all platforms tracked by Altmetric.com), may therefore be useful in characterizing scholarly impact. However, the relationship between traditional measures of impact and altmetrics is unclear for papers in the anatomical sciences education field. The purpose of this study was to quantitatively assess relationships between specific altmetrics, global ALT Scores, and citations for articles in the journal Anatomical Sciences Education (ASE). In addition, qualitative characteristics of highly cited papers (impactful by traditional measures) and papers with high ALT Scores were determined. A database study was performed in November 2017 utilizing the Altmetric Explorer tool (Altmetric.com) for ASE articles published between 2012 and 2017, yielding 340 articles. Citation counts for these papers were collected from Scopus. Correlation coefficients between variables were then determined. Abstracts were textually analyzed and coded to qualitatively identify emerging themes in the top 100 highly cited papers and the papers with the top 100 ALT Scores. The only platforms that featured non‐zero data for the majority (>50%) of articles were Twitter and Mendeley. Most platforms tracked by Altmetric.com were rarely (<20%) used for disseminating ASE articles. ALT Scores were most strongly correlated with Twitter mentions, and featured relatively weak correlations with mentions on Facebook, news articles, or blog posts, as well as citation counts. The only altmetric that strongly correlated with citations was Mendeley download counts. The most dominant theme in papers with both high ALT Scores and high citation counts was the application of modern technologies (ultrasound, 3D printing, and Youtube) in anatomy teaching. Other themes (ex. student‐centered learning, body donation, and modern curriculum development) were present in highly cited papers but were not as pervasive when looking at papers with strong ALT Scores. In conclusion, caution is necessary in terms of interpreting the ALT Score as a global index of attention, since the ALT Score was almost entirely driven by one platform (Twitter). However, specific altmetrics do hold potential in characterizing impact that may reinforce (Mendeley) or complement (Twitter) scholarly impact traditionally depicted by citation counts for papers in the anatomical sciences education field. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0.017 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.036 | 0.042 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".