Content Analysis of a Stratified Random Selection of <i>JVME</i> Articles: 1974–2004
Bibliographic record
Abstract
A content analysis was performed on a random sample (N = 168) of 25% of the articles published in the Journal of Veterinary Medical Education (JVME) per year from 1974 through 2004. Over time, there were increased numbers of authors per paper, more cross-institutional collaborations, greater prevalence of references or endnotes, and lengthier articles, which could indicate a trend toward publications describing more complex or complete work. The number of first authors that could be identified as female was greatest for the most recent time period studied (2000-2004). Two different categorization schemes were created to assess the content of the publications. The first categorization scheme identified the most frequently published topics as admissions, descriptions of courses, the effect of changing teaching methods, issues facing the profession, and examples of uses of technology. The second categorization scheme identified the subset of articles that described medical education research on the basis of the purpose of the research, which represented only 14% of the sample articles (24 of 168). Of that group, only three of 24, or 12%, represented studies based on a firm conceptual framework that could be confirmed or refuted by the study's results. The results indicate that JVME is meeting its broadly based mission and that publications in the veterinary medical education literature have features common to publications in medicine and medical education.
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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 | 0.016 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".