A 45-year Retrospective Content Analysis of <i>JVME</i> Articles
Bibliographic record
Abstract
To study changes in Journal of Veterinary Medical Education ( JVME) content, this article presents the results of an analysis of a purposeful sample ( n = 537) and demographic analysis of all 1,072 articles published from 2005 to 2019. The findings were compared to a prior analysis of articles from 1974 to 2004. Article length increased, as did the number of authors and institutions per article. Female first author numbers grew at a greater rate than the proportion of female faculty at AAVMC-accredited colleges. Close to 85% of articles were by authors in the US, UK, Canada and Australia, while 40 other countries contributed the remainder. The primary topics of papers published from 2005 to 2019 were student affairs (17.3%), professional skills (15.1%), courses and curricula (12.7%), specialty/disciplinary training (12.5%), and technology/information resources (11.5%). The prevalence of articles with an identified research methodology grew from 14.2% in 1974–2004, to 55.9% ( n = 300) in 2005–2019. Among research articles, 54.7% reported an intervention and 70.3% included a comparison. Random assignment to experimental or control conditions occurred in 32 articles (15.2%). Qualitative inquiry expanded, with 16.3% of research articles using this methodology alone. The most cited article was a review paper discussing the human-animal bond. Descriptions of courses and curricula constituted the majority of articles over the journal’s lifespan, while no pattern was discerned between major reports in veterinary education and subsequent publications on that topic. Over the last 45 years, JVME has transitioned from a newsletter to a scholarly publication, with ongoing evolution.
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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 | 0.031 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.057 | 0.035 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".